What are the Top 10 AWS jobs you can get with an AWS certification in 2022 plus AWS Interview Questions

AWS Certified Cloud Practitioner Exam Preparation

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What are the Top 10 AWS jobs you can get with an AWS certification in 2022 plus AWS Interview Questions

AWS certifications are becoming increasingly popular as the demand for AWS-skilled workers continues to grow. AWS certifications show that an individual has the necessary skills to work with AWS technologies, which can be beneficial for both job seekers and employers. AWS-certified individuals can often command higher salaries and are more likely to be hired for AWS-related positions. So, what are the top 10 AWS jobs that you can get with an AWS certification?

1. AWS Solutions Architect / Cloud Architect:

AWS solutions architects are responsible for designing, implementing, and managing AWS solutions. They work closely with other teams to ensure that AWS solutions are designed and implemented correctly.

AWS Architects, AWS Cloud Architects, and AWS solutions architects spend their time architecting, building, and maintaining highly available, cost-efficient, and scalable AWS cloud environments. They also make recommendations regarding AWS toolsets and keep up with the latest in cloud computing.

Professional AWS cloud architects deliver technical architectures and lead implementation efforts, ensuring new technologies are successfully integrated into customer environments. This role works directly with customers and engineers, providing both technical leadership and an interface with client-side stakeholders.

What are the Top 10 AWS jobs you can get with an AWS certification in 2022 plus AWS Interview Questions
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Average yearly salary: $148,000-$158,000 USD

2. AWS SysOps Administrator / Cloud System Administrators:

AWS sysops administrators are responsible for managing and operating AWS systems. They work closely with AWS developers to ensure that systems are running smoothly and efficiently.

A Cloud Systems Administrator, or AWS SysOps administrator, is responsible for the effective provisioning, installation/configuration, operation, and maintenance of virtual systems, software, and related infrastructures. They also maintain analytics software and build dashboards for reporting.

Average yearly salary: $97,000-$107,000 USD


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3. AWS DevOps Engineer:

AWS devops engineers are responsible for designing and implementing automated processes for Amazon Web Services. They work closely with other teams to ensure that processes are efficient and effective.

AWS DevOps engineers design AWS cloud solutions that impact and improve the business. They also perform server maintenance and implement any debugging or patching that may be necessary. Among other DevOps things!

Average yearly salary: $118,000-$138,000 USD

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What are the Top 10 AWS jobs you can get with an AWS certification in 2022 plus AWS Interview Questions
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4. AWS Cloud Engineer:

AWS cloud engineers are responsible for designing, implementing, and managing cloud-based solutions using AWS technologies. They work closely with other teams to ensure that solutions are designed and implemented correctly.

5. AWS Network Engineer:

AWS network engineers are responsible for designing, implementing, and managing networking solutions using AWS technologies. They work closely with other teams to ensure that networking solutions are designed and implemented correctly.

Cloud network specialists, engineers, and architects help organizations successfully design, build, and maintain cloud-native and hybrid networking infrastructures, including integrating existing networks with AWS cloud resources.

Average yearly salary: $107,000-$127,000 USD

6. AWS Security Engineer:

AWS security engineers are responsible for ensuring the security of Amazon Web Services environments. They work closely with other teams to identify security risks and implement controls to mitigate those risks.

Cloud security engineers provide security for AWS systems, protect sensitive and confidential data, and ensure regulatory compliance by designing and implementing security controls according to the latest security best practices.

Average yearly salary: $132,000-$152,000 USD

What are the Top 10 AWS jobs you can get with an AWS certification in 2022 plus AWS Interview Questions
AWS Certified Security Specialty

7. AWS Database administrator:

As a database administrator on Amazon Web Services (AWS), you’ll be responsible for setting up, maintaining, and securing databases hosted on the Amazon cloud platform. You’ll work closely with other teams to ensure that databases are properly configured and secured.

8. Cloud Support Engineer:

Support engineers are responsible for providing technical support to AWS customers. They work closely with customers to troubleshoot problems and provide resolution within agreed upon SLAs.

9. Sales Engineer:

Sales engineers are responsible for working with sales teams to generate new business opportunities through the use of AWS products and services .They must have a deep understanding of AWS products and how they can be used by potential customers to solve their business problems .

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10. Cloud Developer

An AWS Developer builds software services and enterprise-level applications. Generally, previous experience working as a software developer and a working knowledge of the most common cloud orchestration tools is required to get and succeed at an AWS cloud developer job

Average yearly salary: $132,000 USD

11. Cloud Consultant

Cloud consultants provide organizations with technical expertise and strategy in designing and deploying AWS cloud solutions or in consulting on specific issues such as performance, security, or data migration.

Average yearly salary: $104,000-$124,000

12. Cloud Data Architect

Cloud data architects and data engineers may be cloud database administrators or data analytics professionals who know how to leverage AWS database resources, technologies, and services to unlock the value of enterprise data.

Average yearly salary: $130,000-$140,000 USD

What are the Top 10 AWS jobs you can get with an AWS certification in 2022 plus AWS Interview Questions
AWS Data analytics DAS-C01 Exam Prep

Getting a job after getting an AWS certification

The field of cloud computing will continue to grow and even more different types of jobs will surface in the future.

AWS certified professionals are in high demand across a variety of industries. AWS certs can open the door to a number of AWS jobs, including cloud engineer, solutions architect, and DevOps engineer.

Through studying and practice, any of the listed jobs could becoming available to you if you pass your AWS certification exams. Educating yourself on AWS concepts plays a key role in furthering your career and receiving not only a higher salary, but a more engaging position.

Source: 8 AWS jobs you can get with an AWS certification

AWS Tech Jobs  Interview Questions in 2022

Graphs

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1) Process Ordering – LeetCode link…

2) Number of Islands – LeetCode link…

3) k Jumps on Grid – Loading…)

Sort

1) Finding Prefix in Dictionary – LeetCode Link…

Tree

1) Binary Tree Top Down View – LeetCode link…

2) Traversing binary tree in an outward manner.

3) Diameter of a binary tree [Path is needed] – Diameter of a Binary Tree – GeeksforGeeks

Sliding window

1) Contains Duplicates III – LeetCode link…

2) Minimum Window Substring [Variation of this question] – LeetCode link..

Linked List

1) Reverse a Linked List II – LeetCode link…

2) Remove Loop From Linked List – Remove Loop in Linked List

3) Reverse a Linked List in k-groups – LeetCode link…

Binary Search

1) Search In rotate sorted Array – LeetCode link…

Solution:

def pivotedBinarySearch(arr, n, key):
 
    pivot = findPivot(arr, 0, n-1)
 
    # If we didn't find a pivot,
    # then array is not rotated at all
    if pivot == -1:
        return binarySearch(arr, 0, n-1, key)
 
    # If we found a pivot, then first
    # compare with pivot and then
    # search in two subarrays around pivot
    if arr[pivot] == key:
        return pivot
    if arr[0] <= key:
        return binarySearch(arr, 0, pivot-1, key)
    return binarySearch(arr, pivot + 1, n-1, key)
 
 
# Function to get pivot. For array
# 3, 4, 5, 6, 1, 2 it returns 3
# (index of 6)
def findPivot(arr, low, high):
 
    # base cases
    if high < low:
        return -1
    if high == low:
        return low
 
    # low + (high - low)/2;
    mid = int((low + high)/2)
 
    if mid < high and arr[mid] > arr[mid + 1]:
        return mid
    if mid > low and arr[mid] < arr[mid - 1]:
        return (mid-1)
    if arr[low] >= arr[mid]:
        return findPivot(arr, low, mid-1)
    return findPivot(arr, mid + 1, high)
 
# Standard Binary Search function
def binarySearch(arr, low, high, key):
 
    if high < low:
        return -1
 
    # low + (high - low)/2;
    mid = int((low + high)/2)
 
    if key == arr[mid]:
        return mid
    if key > arr[mid]:
        return binarySearch(arr, (mid + 1), high,
                            key)
    return binarySearch(arr, low, (mid - 1), key)
 
# Driver program to check above functions
# Let us search 3 in below array
if __name__ == '__main__':
    arr1 = [5, 6, 7, 8, 9, 10, 1, 2, 3]
    n = len(arr1)
    key = 3
    print("Index of the element is : ", \
          pivotedBinarySearch(arr1, n, key))
 
# This is contributed by Smitha Dinesh Semwal

Arrays

1) Max bandWidth [Priority Queue, Sorting] – Loading…

2) Next permutation – Loading…

3) Largest Rectangle in Histogram – Loading…

Content by – Sandeep Kumar

#AWS #interviews #leetcode #questions #array #sorting #queue #loop #tree #graphs #amazon #sde —-#interviewpreparation #coding #computerscience #softwareengineer

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Tech Jobs and Career at FAANG (now MAANGM): Facebook Meta Amazon Apple Netflix Google Microsoft

The FAANG companies (Facebook, Amazon, Apple, Netflix, Google, and Microsoft) are some of the most sought-after employers in the tech industry. They offer competitive salaries and benefits, and their employees are at the forefront of innovation.

The interview process for a job at a FAANG company is notoriously difficult. Candidates must be prepared to answer tough technical questions and demonstrate their problem-solving skills. The competition is fierce, but the rewards are worth it. Employees of FAANG companies enjoy perks like free food and transportation, and they often have the opportunity to work on cutting-edge projects.

If you’re interested in a career in tech, Google, Facebook, or Microsoft are great places to start your search. These companies are leaders in their field, and they offer endless opportunities for career growth.

This blog is about Clever Questions, Answers, Resources, Feeds, Discussions about Tech jobs and careers at MAANGM companies including:

  • Meta (Facebook)
  • Apple
  • Amazon
  • AWS
  • Netflix
  • Google (Alphabet)
  • Microsoft
Tech Jobs and Career at FAANG (now MAANGM): Facebook Meta Amazon Apple Netflix Google Microsoft  How to get a tech job at MAANG (Meta, Amazon, Apple, Netflix, Google) |  TechGig

Top-paying Cloud certifications provided by MAANGM:

According to the 2020 Global Knowledge report, the top-paying cloud certifications for the year are (drumroll, please):

  • Google Certified Professional Cloud Architect — $175,761
  • AWS Certified Solutions Architect – Associate — $149,446
  • AWS Certified Cloud Practitioner — $131,465
  • Microsoft Certified: Azure Fundamentals — $126,653
  • Microsoft Certified: Azure Administrator Associate — $125,993

FAANG – MAANGM Compensation

Legend – Base / Stocks (Total over 4 years) / Sign On


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Google (Alphabet)

– 145/270/30 (2017, L4)
– 150/400/30 (2018, L4)

*Google’s target annual bonus is 15%. Vesting is monthly and has no cliff.

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Facebook ( Meta)
– 115/160/100 (2017, E3)

– 160/300/70 (2017, E4)
– 145/220/0 (2017, E4)
– 175/250/0 (2017, E5)

– 210/1000/100 (2017, E6)

*Facebook’s target annual bonus is 10% for E3 and E4. 15% for E5 and 20% for E6. Vesting is quarterly and has no cliff.

LinkedIn (Microsoft)

– 125/150/25 (2016, SE)
– 120/150/10 (2016, SE)

– 170/300/30 (2016, Senior SE)
– 140/250/50 (2017, Senior SE)

Apple

– 110/60/40 (2016, ICT2)
– 120/100/21 (2017, ICT3)
– 135/105/20 (2017, ICT3)

– 160/105/30 (2017, ICT4)

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Amazon (AWS)
– 103/65/52 (2016, SDE I)
– 110/200/50 (2016, SDE I)
– 135/70/45 (2016, SDE I)

– 160/320/185 (2018, SDE III)

*Amazon stocks have a 5/15/40/40 vesting schedule and sign on is split almost evenly over the first two years*

Microsoft
– 106/120/15 (2016, SDE)
– 107/90/35 (2016, SDE)

Twitter

– 130/200/20 (2016, SWE1)

– 160/600/50 (2017, SWE II)

Uber

– 110/180/0 (2016, L3)
– 110/150/0 (2016, L3)

– 140/590/0 (2017, L4)

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Lyft

– 135/260/60 (2017, L3)

– 170/720/20 (2017, L4)
– 152/327/0 (2017, L4)
– 175/480/0 (2017, L4)

Dropbox

– 167/464/10 (2017, IC2)
– 160/250/10 (2017, IC2)
– 160/300/50 (2017, IC2)

That’s my guess. It hasn’t changed when Google became Alphabet.

FAANG stared as FANG circa 2013. The 2nd A became customary around 2016 as it wasn’t clear whether A referred to Apple or Amazon. Originally, FANG meant “large public, fast growing tech companies”. Now in 2021, the scope of what FANG referred to just doesn’t correspond to these 5 companies.

From an investment perspective (which is the origin of FANG) Facebook stock has grown the slowest of the 5 companies over the past 5 years. And they’re all dwarfed by Tesla.

From an employment desirability perspective (which is the context where FAANG is most used today). Microsoft is very similar to the group. It wasn’t “cool” around 2013 but its stock actually did better than Facebook or Alphabet over the past five years. Other companies like Airbnb, Twitter or Salesforce offer the same value proposition to employees, that is stability and tradable equity as part of the compensation.

FAANG refers to a category more than a specific list of companies.

As a side note, I expect people to routinely call the company Facebook, just like most people still say Google when they really mean Alphabet.

The technical interviews at FAANG companies, in the grand scheme, aren’t very difficult.

People frequently fail FAANG interviews because they choke — they experience anxiety and just forget their knowledge — or they don’t know the material to begin with.

Inverting a binary tree, matching up pairs of brackets, finding the duplicate in an array of distinct integers, etc., are all weeder-questions that should be solvable in 5–10 minutes, if you’re the type to suffer from interview jitters. You should know which data structures to use, intuitively, and you should be doing prep work to cover your knowledge gaps if you don’t.

Harder questions will take longer, but ultimately, you’ll have 45 minutes or so to solve 2–3 questions.

Technical interviews at FAANG companies are only difficult if you have shaky computer science fundamentals. Luckily, the process for cracking the code interview *cough* is very well-documented, hence, you only need to follow the already established strategies. If you’re interested in maximizing income while prioritizing career growth, it behooves you to spend a month or two studying these strategies.

The difficulty of the interview is going to vary more interviewer to interviewer, than company to company. Also, how difficult the questions are is not directly related to how selective the process is; the latter being heavily influenced by business factors currently affecting these companies and what are their current hiring plans.

Comments:

#1:  So, how do know you this? You don’t. An affirmative answer to this question can only come from data.

#Answer #1:  Fair question. I have been very involved in interviewing in a number of large tech cos. I have read, by now, thousands of interview debriefs. I have also interviewed a fair amount as a candidate, although I have not interviewed in each of the “FAANG” and I have definitely be more often on the interviewing side.

As such, I have seen for the same position, very easy questions and brutally difficult ones; I have seen very promising candidates not brought to onsite interviews because the hiring organization didn’t currently have resources to hire, but also ok-ish candidates given offers because the organization had trouble meeting their hiring targets. As a candidate I also experienced: easy interview exercises but no offer, very hard interview exercises and offer (with the caveat that I never know exactly how well I do, but I certainly can tell if a coding question or a system design question is easy or hard).

So. I am well aware that it’s still anecdotal evidence, but it’s still based on a fairly large sample of interviews and candidates.

#Reply to #1: Nope, you’re wrong. I have experience in the interview process at Amazon and Microsoft and have a different conclusion. Moreover, “experts” in lots of disparate fields make claims that are a bunch of bullcrap due to their own experiential biases. Additionally, you would need to be involved at all of the companies listed, not just some of the them, for that experience to be relevant in answering this question. We need to look at the data. If you don’t have data, I will not trust you just because of “your experience”. I don’t think it’s possible for Jerry C to have the necessary information to justify the confidence that is projected in this answer.

What you need is not so much a list of “incidents” but more generally some self-awareness on what you care about and how you’ve progressed and how you see your career.

The best source for this material is your performance reviews. Ideally you also kept some document about your career goals and/or conversation with your manager. (If you haven’t such documents, it’s never too late to start them!).

You should have 5–6 situations that are fairly recent and that you know on the back of your hand. These must include something difficult, and some of these situations must be focused on interpersonal relationships (or more generally, you should be aware of more situations that involved a difficult interpersonal relation). They may or may not have had a great outcome – it’s ok if you didn’t save the day. But you should always know the outcome both in terms of business and on your personal growth.

Once you have your set of situations and you can easily access these stories / effortlessly remember all details, you’ll find it much easier to answer any behavioural question.

To take a few steps back, there are 2 things that interviewers care about in behavioural interviews – whether the candidate has the right level, and whether they exhibit certain skillsets.

When you look at this question from the first angle, it’s important to be able to present hard problems on which it’s clear what the candidate’s personal contribution was. Typically, later projects are better for that than earlier ones.

Now, in terms of skillsets, this really depends company by company but typically, how well a candidate is able to describe a problem especially to someone with a different expertise, and whether they spontaneously go on to describe impact metrics, goes a long way.

So great answer: hard, recent, large scale project, that the candidate is able to contextualize (why was is important, why was it hard, what was at stake), where they are able to describe what they’ve done and what was the potential impact, and what were the actual consequences.

Not so great answer: a project that no one asked the candidate to do, but which they insisted on doing because they thought it was cool/interesting, on which they worked alone and which didn’t have any business impact. Source.

This question (like many other things in life) is much more complicated than it appears on the surface. That’s because it is conflating several very different issues, including:

  • What is retirement?
  • What is “early”?
  • At what age do most software engineers stop working in that role?
  • How long do employees stay on average at the FAANGs?

In the “old” days (let’s arbitrarily call that mid-20th century America), the typical worker was white, male and middle class, employed on location at a job for 40–50 hours a week. He began his working career at 18 (after high school) or 22 (after college), and worked continuously for a salary until the age of 65. At that time he retired (“stopped working”) and spent his remaining 5–10 years of life sitting at home watching tv or traveling to places that he had always wanted to visit.

That world has, to a large extent, been transmogrified over the past 50 years. People are working longer, changing employment more frequently, even changing careers and professions as technology and the economy change. The work force is increasingly diverse, and virtually all occupations are open to virtually all people. Over the past two years we have seen that an astonishing number of jobs can be done remotely, and on an asynchronous basis. And all of these changes have disproportionately affected software engineering.

So, let’s begin by laying out some facts:

  • When people plan to retire is a factor of their generation: Generation Y — ages 25 to 40 — plans to retire at an average age of 59. For Generation X — now 41 to 56 — the average age is 60. Baby boomers — who range from 57 to 75 — indicated they plan to work longer, with an average expected retirement age of 68.[1]
  • The average actual retirement age in the US is 62[2]
  • Most software engineers retire between the ages of 45 and 65, with less than 1% of developers working later than 65.[3]
  • But those numbers are misleading because many software engineers experience rapid career progression and move out of a pure development role long before they retire.
  • The average life expectancy in Silicon Valley is 85 years.[4]
  • The tenure of employment at the FAANGs is much shorter than than one might imagine. Unlike in the past, when a person might spend his or her entire career working for one or two employers, here are the average lengths of time that people work at the FAANGs: Facebook 2.5 years, Google 3.2 years, Apple 5 years.[5]

Therefore, if the question assumes that a software engineer gets hired at a FAANG company in his or her 20s, works there for 20 or 30 years as a coder, and then “retires early”, that is just not the way things work.

Much more likely is the scenario in which an engineer graduates from college at 21, gets a masters degree in computer science by 23, starts as a junior engineer at a small or large company for a few years, gets hired into a FAANG by their early 30s, spends 3–5 years coding there, is recruited to join a non-FAANG by their early 40s in a more senior role, and moves into management by their late 40s.

At that point things become a matter of personal preference: truly “retire”, start your own venture; invest in cryptocurrency; move up to senior management; begin a second career; etc.

The fact is that software engineering at a high level (such as would warrant employment at a FAANG in the first place) pays very well in relative terms, and with appropriate self-control and a moderate lifestyle would enable someone to “retire” at a relatively early age. But paradoxically, that same type of person is unlikely to do so.

No. In fact Google’s a really poor workplace by comparison with most others I’ve had in my career. Having a private office with a door you can close is a real boon to doing thoughtful, creative work, and having personal space so that you can feel psychologically safe is important too.

You don’t get any of that at Google, unless you’re a director or VP and your job function requires closed-door meetings. I have a very nice, state-of-the-art standing desk, with a state-of-the-art monitor, and the only way for me to avoid hearing my tech lead’s conversations is to put headphones on. (You can get very nice, state-of-the-art headphones, too.)

On the other hand, I also have regular access to great food, and an excellent gym, and all the La Croix water I can drink. I get to work on the most incredible technological platform on earth. And the money’s good. But heaven on earth? Nah. That’s one of the reasons the money’s good.

A new grad software engineer (L3) at Google makes a salary around $193,000 including stock compensation and bonus. The industry is getting a lot more competitive and top companies such as Google have to make offers with really generous stock packages. The below diagram shows a breakdown for the salary. View all the crowdsourced reports as well as other levels on Levels.fyi.

Hope that helps!

Having recent left Google for a new startup I have to agree that the most-missed perk is the food. It’s not so much that it’s free — you can get lunch for about $10 per day so the cost is not a huge deal. There is simply nowhere you can go, even in a Silicon Valley city like Mountain View, that has healthy low-fat, varied choices that include features like edible fruits and vegetables. The food is even color-coded (red/yellow/green) based on how healthy it is (it always bothered me that the peanut-butter cups are red….).

Outside of Google you end up having muffins for breakfast and pizza for lunch. It tastes good but it’s not the same to your body.

But beyond just the food, the long term health impact of the set of perks at Google is huge. There is nothing better than being able to come in early, work out at the (free) gym by your office, shower (with towels provided as noted by others), then have eggs (or egg whites if you prefer) and toast (or one of a dozen other breakfasts). Source

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Everyone has a study plan and list of resources they like to use. Different plans work for different people and there is no one size fits all.

This by no means is the only list of resources to join a larger technology company. But it is the list of resources I used myself to prepare for all my technology interviews.

Quick Background

I’m a current engineer at Microsoft who previously worked at Amazon for 1 year each respectively. I don’t have a master’s degree and I graduated from NYU, not an Ivy League. I’ll soon be joining Google and the following resources is how I got there.

Yes, the purchasable resources are affiliate links that help support this blog. Regardless, these are the resources I’ve used both purchasable and free.

Coding Resources

Cracking the Coding Interview (CTCI)

This is the simplest book to get anyone started in studying for coding interviews.

If you’re an absolute beginner, I recommend you to start here. The questions have very details explanations that are easy to understand with basic knowledge of algorithms and data structures.

Elements of Programming Interviews (PythonJavaC++)

If you’re a little more experienced, every question in this book is at the interviewing level of all large technology companies.

If you’ve mastered the questions in this book, then you are more than ready for the average technology interview. The book is not as beginner friendly as CTCI but it does include a study plan depending on how much you need to prepare for your interviews. This is my personal favorite book I carried everywhere in university.

NeetCode blind 75 — YouTube

Blind has a list of 75 questions that is generally enough to solve most coding interviews. It’s a very curated and focused list for the most essential algorithms to leverage your time.

The playlist above is one of the clearest explanations I’ve ever seen and highly recommend if you need an explanation on any of the problems.

CSES Problem Set — Tasks

These problems are hard. Really hard for anyone who hasn’t practiced algorithms and is not beginner friendly. But if you are able to complete the sorting and searching section, you will be more capable than the average LeetCode user and be more than ready for your coding interview.

Consider this if you’re comfortable with LeetCode medium questions and find the questions in CTCI too easy.

Algorithm Learning

Photo by Андрей Сизов on Unsplash

Introduction to Algorithms (4th Edition)

This is the most common and best textbook anyone could use to learn algorithms. It’s also the textbook my university used personally to learn the core and essential algorithms to most coding problems.

The 4th edition was recently released and is still relevant to MIT students. If you need structure and a traditional classroom setting to study, follow MIT’s algorithm course here.

William Fiset — Graph Theory

Graph theory does come up in interviews (and was a question I had at both Bloomberg and Google). Stay prepared and follow William Fiset’s graph theory explanation.

The diagrams are comprehensive and the step-by-step explanations are the best I’ve ever seen on the topic.

CSES.fi Handbook

This handbook is for people who are strongly proficient with most Leetcode algorithms. It’s a free resource that strongly complements the CSES.fi curriculum.

Competitive Programming 4th Ed.

For the most experienced algorithm enthusiasts, this book will cover every niche data structure and algorithm that could possibly be asked in any coding interview. This level of preparation is not generally needed for FAANG type companies but can show up if you’re considering hedge fund type companies.

System Design

Photo by Med Badr Chemmaoui on Unsplash

The System Design Interview (Vol. 1Vol. 2Online Course + Community)

In my opinion, you will be more than ready for any system design interview using these resources. The diagrams are clear and the explanations are as simple as possible in each book to help you learn system design concepts quickly.

I recommend the online course personally because yes the content from both books is great to own, it’s the online community discord you get access to that makes the yearly subscription worth it. The discord includes mock interview buddies, salary discussion, and over view on each system design topics to study with other users on.

System Design Primer

The system design primer is the best free resource on all things system design. Dig deep into the Git repository and you will learn everything you need to know on system design. It’s all curated in a single repository and the clearly structured to give you a guided curriculum.

Educative’s System Design Interview

This quick overview on system design is great to review if you’re in a rush. The read typically takes users 45 minutes but you’ll be left knowing more system design than the average engineer.

Give it a read. If concepts are unclear or confusing, that might be a sign you’re not ready for interviews.

Object Oriented Design

Tech Jobs and Career at FAANG (now MAANGM): Facebook Meta Amazon Apple Netflix Google Microsoft
Photo by Christina @ wocintechchat.com on Unsplash

Design Patterns: : Elements of Reusable Object-Oriented Software

Regardless if you’re learning design patterns for the object-oriented programming interview, you will need to know design patterns as a software engineer at these large companies.

The book is the origin of the world’s most common design patterns today and showing proficiency in these for your object oriented interview is a requirement for certain large technology companies like Amazon.

Head First Design Patterns

The above resource is dense and written in language that’s hard to understand. While the original source material in design patterns is great, it doesn’t help much if it’s difficult to understand.

Consider Head First Design patterns to study a simplified explanation of those common design patterns. It might not be as in-depth as the original source material, but your understanding in design patterns will be more than enough to crack any object-oriented interview.

Closing Thoughts

Honestly, I did not go through all of these resources from cover to cover. If you do, I’m sure you wouldn’t need to study for another interview again. But likely we don’t have the time to do that so make sure that once you understand the core concepts in the any of the above categories that you invest your time moving on to the next.

Again, these are the resources I used and is not at all inclusive of anyone else’s study plan.

My Google L4 interview experience by Alex Nguyen

Quick Background

Recruiter Screen

Phone Screen

Technical Onsite

Coding Interview #1

Coding Interview #2

Coding Interview #3

Coding Interview #4

Photo by Tim Gouw on Unsplash

The Googliness interview

Offer

Yes, it is. That’s a very good sign.

That means you interviewed well. Someone else interviewed better for the first role, but the recruiter sees that there other roles for which you might be a better fit.

The eight interviews is a sign that someone in the process wanted you specifically for some role.

I think there may be two different things going on.

First, are you sure whether it’s a FAANG recruiter, or someone from an external sourcing firm which is retained by a FAANG company? I had this experience where someone reached out on LinkedIn and said they were recruiting for a Google role and passed along a job description. As I started asking them questions, it became clear that they just wanted me to fill out an application so that they can pass it to someone else. Now, as it happens, I am a former Google employee, so it quickly became clear that this person was not from Google at all, but just retained to source candidates. The role they wanted me to apply for was not in fact suitable, despite their claim that they reached out to me because I seemed like a good match.

If you are dealing with a case like this, probably what happens is that they source very broadly, basically spamming people, on the chance that some of the people they identify will in fact be a good fit. So they would solicit a resume, pass it to someone who is actually competent to judge, and that person would reject. And the sourcing firm will often ghost you at this point.

If you are dealing with an actual internal recruiter, I think it can be a similar situation. A recruiter often doesn’t really know if you are a fit or not, and it will often be some technical person who decides. That person may spend 30 seconds on your resume and say “no”. And positions get filled too, which would cause everyone in the pipeline to become irrelevant.

In such cases there is no advantage for the recruiter to further interact with you. Now, every place I worked with, I am pretty sure, had a policy that if a recruiter interacted with the candidate at all, they were supposed to formally reject them (via email or phone). But I imagine there’s very little incentive for a recruiter to do it, so they often don’t. And as a candidate, you don’t really have any way to complain about it to the company, unless you have a friend or colleague on the inside. If you do, I suggest you ask them, and it may do some good, if not to you (you are rejected either way), at least to the next applicant.

As a software engineer or programmer, what’s the dumbest line of code you’ve seen in a codebase?

It’s not actually a line of code, so to speak, but lines of code.

I work in Salesforce, and for those who are not familiar with its cloud architecture, a component from QA could be moved to production only if the overall test coverage of the production is 75% or more. Meaning, if the total number of lines of code across all components, including the newly introduced ones, is 10000, enough test classes must be written with appropriate test scenarios so as to cover at least 7500 lines of the lump. This rule is enforced by Salesforce itself, so there’s no going around it. Asserts, on the other hand, could be done without.

If the movement of your components causes a shift in balance in production and tips its overall coverage to below 75%, you are supposed to work on the new components and raise their coverage before deployment. A nightmare of sorts, because there is a good chance your code is all clean and the issue occurs only because of a history of dirty code that had already gone in over years to drag the overall coverage to its teetering edges.

Someone in my previous company found out a sneaky way to smuggle in some code of his (or hers) without having to worry about this problem.

So this is simple math, right? If you have got 5000 lines of code, 3750 must be covered. But what if I have managed to cover only 2500 (50%) and my deadline is dangerously close?

Simple. I add 5000 lines of unnecessary code that I can surely cover by just one function call, so that the overall line number now is 10000 and covered lines are 7500, making my coverage percentage a sweet 75.

For this purpose they introduced a few full classes with a lone method in each of them. The method starts with,

Integer i = 0;

and continues with a repetition of the following line thousands of times.

i++;

And they had the audacity to copy and paste this repetitive ‘code’ throughout a bulky method and across classes in such a reckless manner that you could see a misplaced tab in first line replicated exactly in every 100th line or so.

Now all that is left for you to do is call this method in a test class, and you can cover scores of lines without breaking a sweat. All the code that actually matters may lie untested in automated coverage check, glaring red if one should care to take a look at, but you have effectively hoodwinked Salesforce deployment mechanism.

And the aftermath is even crazier. Seeing the way hoards of components could be moved in without having to embark on the tedious process of writing test classes, this technique acquired a status equivalent to ‘Salesforce best practices’ in our practice. In almost all the main orgs, if you search for it, you can find a class with streams of ‘i++;’ flowing along the screen for as far as you have the patience to scroll down.

Well, these cloaked dastards remained undetected for years before some of the untested scenarios started reeking. More sensible developers fished out the ‘i++;’ classes, raised the alarm and got down to clean up the mess. Just removing those classes drove the overall production coverage to abysmal low, preventing any form of interaction with production. What can I say, that kept many of us busy for at least a month.

I wouldn’t call the ‘developers’ that put this code in dumb. I would rather go for ‘wicked’. The higher heads and testers who didn’t care to look while this passed under their noses do qualify as dumb.

And the code… Man, that’s the dumbest thing I’ve ever seen.

For Google, other than data structure and algorithms, what else should I prepare for in an interview?

Ask your recruiter.

If you are in the pipeline and you have interviews scheduled, then your recruiter will know exactly what loop will be set up for you and what kind of questions you may have. Recruiters try to get their candidates all the information they need to approach the interviews at the top of their potential, so ask the everything you need to know.

The actual answer depends on the candidate level and profile, the composition of the interviews is pretty much bespoke.

Would Elon Musk pass the Google, Amazon, or Facebook technical interview for the software engineer position?

I think it is likely he will pass the interview if the job description includes the following text:

The successful candidate will have built a brand new car and launched it into interplanetary orbit, using a rocket that they also built. 

But will he even want the job?

FREE AWS, Azure, GCP Training & Certification Preparation Videos. FAANG IT SDE – Architect job interviews Prep

https://www.youtube.com/channel/UCjxhDXgx6yseFr3HnKWasxg

How difficult is it to find highly talented software developers?

Dev: Alright, let the competition begin!
Startup A: We will give you 50% of the revenue!
Startup B: To hell with it, we will give you 100%!
Startup A: Eh… we will give you 150%!

TL;DR: Nearly impossible. If you are a Google-sized company, of course. Totally impossible in other cases.

I run an outsourcing company. Our statistics so far:

  • 500 CVs viewed per month
  • 50 interview invitations sent per month
  • 10 interviews conducted per month
  • 1 job offer made (and usually refused) per month

And here we are looking for a mid-level developers in Russia.

Initially we wanted to hire some top-notch engineers and were ready to pay “any sum of money that would fit on the check”. We sent many invitations. Best people laughed at us and didn’t bother. Those who agreed – knew nothing. After that we had to shift our expectations greatly.

Still, we manage to find good developers from time to time. None of them can be considered super-expert, but as a team they cooperate extremely effectively, get the job done and all of them have that engineering spirit and innate curiosity that causes them to improve.

This is as good as an average company can get.

What is something worth knowing that people working at Google know and others don’t?

It takes constant human effort to keep sites like Google and Gmail online. Right now a Google engineer is fixing something that no one will ever know was broken. Some server somewhere is running out of memory, a fiber link has gone down, or a new release has a problem and needs to be rolled back. There are careful procedures, early warnings, and multiple layers of redundancy to ensure that problems never become visible to end users, but.

Sometimes problems do become visible but not in a way that an individual user can attribute to the site. A request might not get a prompt response, or any at all, but the user will probably blame the internet or their computer, not the site. Google itself is very rarely glitchy, but services like image search do sometimes have user visible problems.

And then of course, very rarely, a giant outage brings down something giant like YouTube or Google Cloud. But if it weren’t for an army of very smart, very diligent people, outages would happen much more often.

What do 10x software developers understand that other programmers don’t?

It’s what they don’t understand. 10x software engineers don’t really understand their job description.

They tend to think all these other things are their responsibility. And they don’t necessarily know why they’re doing all these other things. They just sense that it’s the right thing to do. If they spot something is wrong, they will just fix it. Sometimes it even seems like they’re not in control of what they do. It’s like a conscientiousness overdose.

10x engineers are often all over the code base. It is like they had no idea they were just part of one eng team.

Why don’t big tech companies like Google, Microsoft, and Facebook care about work experience and previous projects when interviewing software engineering candidates and rely completely on programming problems?

Thanks for the A2A.

I don’t think the premise behind the question is entirely true. These companies rely completely on programming problems with junior candidates that are not expected to have significant experience . Senior candidates do, in fact, get assessed based on their experience, although it might not always feel like it.

Let me illustrate this with an interview process I went through when interviewing for one of the aforementioned companies (AFAIK it’s typical for all the above). After the phone screen, there was a phone site interview with 5 consecutive interviews – 2 whiteboard coding + 2 whiteboard architecture problems + 1 behaviour interview. On the surface, it looks like experience doesn’t play a part, but, SURPRISE, experience and past projects play part in 3 interviews out of 5. A large part of the behavioural interview was actually discussing past projects and various decisions. As for the architecture problems – it’s true that the problem discussed is a new one, but those are essentially open ended questions, and the candidates experience (or lack thereof) clearly shines through. Unlike the coding exercises, these questions are almost impossible to solve without tackling something similar in the past.

Now, here a few reasons to why the emphasis is still on solving new problems and not diving into the candidates home territory, in no particular order:

  • Companies do not want to pass over strong candidates that just happen to be working on some boring stuff.
  • Most times companies do not want to clone a system that the candidate has worked on, so the ability to learn from experience, and apply it to new problems is much more valuable.
  • When the interviewer asks different candidates to design the same system, they can easily compare different candidates against one another. The interviewer is also guaranteed to have a deep understating of the problem they want the candidate to solve.
  • People can exaggerate (if not outright lie) their role in working on a particular project. This might be hard to catch-on in one hour, so it’s to avoid in the first place.
  • (This one is a minor concern, but still) Large companies hire by committee, where interviewers are gathered from the whole company. The fact that they shouldn’t discuss previous projects, removes the need to coordinate on questions, by preventing a situation where two interviewers accidentally end up talking about the same system, and essentially doing the interview twice.

I hope that adds some clarity.

As a teenager, what can I do to become an engineer/entrepreneur like Elon Musk? What skills can I start learning to succeed as an engineer/entrepreneur?

Originally Answered: What can I, currently 17 years old, do to become an engineer/entrepreneur like Elon Musk?

This is a quick recap of my earlier response to a similar question on Quora:

I would recommend that you take a close look at the larger scheme of things in your life, by spending some time and effort to design your life blueprint, using Elon Musk as your inspiration and/or visual model.

By the way, here’s my quick snapshot of his beliefs and values:

1) Focus on something that has high value to someone else;

2) Go back to first principles, so as to understand things more deeply and widely, especially their implications;

3) Be very rigourous in your own self analysis; constantly question yourself, especially on the practicality of the idea(s) you have;

4) Be extremely tenacious in your pursuits;

5) Put in 100 hours or more every week, as sweat equity of intense efforts and focused execution count like hell;

6) Constantly think about how you could be doing better, faster, cheaper and smarter;

7) Relentlessly and ruthlessly think about how to make a better world;

Again, here’s my quick snapshot of his unique traits and characteristics:

1) Be a voracious reader.

2) Be intrinsically driven.

3) (F)ollow (o)ne (c)ourse (u)ntil (s)uccess. That’s Focus!

4) Develop a steadfast problem solving attitude.

5) Employ a physics-mind or first principles in problem solving.

6) Work doubly hard, and a lot, and diligently.

7) Welcome negative feedback.

Nonetheless, here is a simple template:

1) First and foremost, know exactly what you want, in terms of compelling, inspiring and overarching long-range goals and objectives:

a) what do I want to be?

b) what do I want to do?

c) what do I want to have?

d) what do I want to improve?

e) what do I want to change?

in tandem with the following major life dimensions in your life:

i) academic pursuit;

ii) mental development;

iii) career aspirations;

iv) physical health;

v) financial wealth;

vi) family relationships;

vii) social networking;

viii) recreational ventures (including hobbies, interests, sports, vacations, etc.);

ix) spiritual development (including contributions to society, volunteering, etc.);

2) Translate all your long-range goals and objectives in (1) into specific, prioritised and executable tasks that you need to accomplish daily, weekly, monthly, quarterly and even annually;

3) With the end in mind as formulated in (1) and (2), work out your start-point, endpoint and the developmental path of transition points in between;

4) Pinpoint specific tasks that you need to accomplish at each transition point till the endpoint;

5) Establish metrics to measure your progress, or milestone accomplishments;

6) Assign and allocate personal accountability, as some tasks may need to be shared, e.g. with team members, if any;

7) Identify and marshal resources that are required to get all the work done;

[I like to call them the 7 M’s: Money; Methods; Men; Machines; Materials; Metrics; and Mojo!]

8) Schedule a timetable for completion of each predefined task;

9) Highlight potential problems or challenges that may crop up along the Highway of Life, as you traverse on it;

10) Brainstorm a slew of possible strategies to deal with (9);

This is your contingency plan.

11) Institute some form of system, like a visual Pert Chart, to track, control and monitor your forward trajectory, as laid out in your systematic game plan, in conjunction with all the critical elements of (4) to (10);

12) Follow-up massively and follow-through consistently your systematic game plan;

13) Put in your sweat equity of intense effort and focused execution;

14) Stay focused on your strategic objectives, but remain flexible in your tactical execution;

Godspeed to you, young man!

Why may a software engineer struggle in a Google/Facebook onsite interview despite solving most of the LeetCode questions?

For a whole bunch of reasons.

You aren’t so stressed and nervous when you are practicing LeetCode, because your career doesn’t depend on how well you do while solving LeetCode.

When solving LeetCode, you aren’t expected to talk to the interviewer to get clarifications on the problem statement or input format. You aren’t expected to get hints and guidance from the interviewer, and to be able to pick them up. You aren’t expected to be able to communicate with other human beings in general, and to be able to talk about technical details of your solution in particular. You aren’t expected to be able to prove and explain your idea in clear, structured way. You aren’t expected to know how to test your solution, how to scale it, or how to adjust it to some unexpected additional constraints or changes. You may not be able to simply get constraints on input size and use them to figure out what is the complexity of expected solution. You have limited amount of time, so if you slowly got through most of the LeetCode, you may still struggle to get stuff done in 45 minutes. And many more… For all these things, you don’t need them to solve LeetCode, so you usually don’t practice them by solving LeetCode; you may not even know that you need to improve something there.

To sum it up: two main reasons are:

  1. Higher stakes.
  2. Lack of skills that are required at typical Google/Facebook interview, but not covered by solving LeetCode problems on your own.

You should also keep in mind that LeetCode isn’t the list of problems being asked at Google or Facebook interviews. If anything, it is more of a list of problems that you aren’t going to be asked, because companies ban leaked questions 🙂 You may get a question that is surprisingly different from what you did at LeetCode.

And sometimes you simply have a bad day.

I failed all technical interviews at Facebook, Google, Microsoft, Amazon, and Apple. Should I give up the big companies, keep improving my algorithm skills, and try some small startups?

Originally Answered: I failed all technical interviews at Facebook, Google, Microsoft, Amazon and Apple. Should I give up the big companies and try some small startups?

Wanted to go Anonymous for obvious reasons.

Reality is stranger than Fiction.

In 2010: After graduation, I was interviewed by one of the companies mentioned above for an entry level Software Engineering Role. During the interview, the person tells me: ‘You can never be a Software Engineer’. Seriously? Of-course I didn’t get hired.

In 2013: I interviewed again with the same company but for a different department and got hired.

Fast Forward to 2016 Dec: I received 2 promotions since 2013 and now I am above the grade level of the guy who interviewed me. I remember the date, Dec 14 2016, I went to his desk and asked him to go out for a coffee. Initially he didn’t recognize me but later he did and we went out for a coffee. Needless to say, he was apologetic for his behavior.

For me, it felt REALLY GOOD. Its a story I’ll tell my Grandkids! 🙂

I have 3 years of experience as a software developer. Should I expect algorithms at an interview at FAANG + Microsoft?

Big tech interviews at FAANG companies are intended to determine – as much as possible – whether you’ve got the knowledge and attributes to be a successful employee. A big part of that for software developers is familiarity with a good set of data structures and algorithms. Interview loops vary, but a good working knowledge of common algorithms will almost always come in handy for both interviews and the job.

Algorithm-related to questions I was asked in my first five years, or that I ask people with less than 5 years: sorting, searching, applying hashes correctly, mapping, medians and averages, trees, linked lists, traveling salesman (I was asked this a couple times, never asked it), and many more.

I never recommend an exhaustive months-long review before an interview, but it’s always a good idea to make sure you’re current on your basics: hash tables and sets, string operations, working with arrays and vectors and lists, binary trees, and linked lists.

For more information on how interviews work and what to expect for big tech interviews, you may want to watch some of my videos in this playlist: Big Tech InterviewsVideos about interviewing at the big tech companies like Microsoft, Google/Alphabet, Amazon, and Facebook.

How true is it that learning Python programming language first will make it harder to learn other programming languages later down the line?

Compared to other modern languages, python has two features that make it attractive, and then also make learning a second language difficult if you started with python. The first is that, despite some minor steps to allow annotation, python is loosely and dynamically typed. The second is that python provides a lot of syntactic sugar; this is shorthand, like a map function, where you can apply a function to each element in a data structure.

Do these features make it harder to switch to another language that is strongly and statically typed? For some people, yes, and for others, no.

Some programmers are naturally curious what’s happening under the hood. How are data being represented and manipulated? Why does an operation produce one type of result in one situation, and another type of result in another situation? If you are the kind of person who asks these questions, you are more likely to have an easier time transitioning. If you are a person who finds these questions uninteresting or even distasteful, transitioning to another language can be very painful.

As a software engineer, how do you make your resume stand out from the crowd?

I have excellent skills and experience on my resume, which makes it stand out.

Seriously, there is no magical spell that will make a crappy resume attractive to recruiters. Most people give up believing in magic after they are 5 or 6 years old. A software engineer who believes in magic is not a good candidate for hire.

What are some secrets about working for big tech companies that you didn’t know before joining those companies?

All those complaints you have about their products? The people working there complain about the same exact things. Microsoft employees complain about how slow Outlook is. Google employees complain about everything changing all the time. Salesforce employees complain about how hard our products are to use.

So why don’t we do something about it? There are a few possible answers:

  1. We are actively doing something about it right now and it will be fixed soon.
  2. The problem is technically difficult to fix. For example, it’s currently beyond the state of the art to change the wake word (“Alexa”/”OK Google”) to a user-selected word. A variation of this is the problem that’s more expensive to fix than the amount of annoyance saved.
  3. The team responsible for that functionality has problems. Maybe they have a bad manager or have been reorged a lot, and as a result they haven’t been doing a good job. Even once the problem is solved, it can take a long time to catch up.
  4. The problem is related to making money. For example, Microsoft used to have a million different versions of Office, each including different programs and license restrictions. It was super confusing. But the bean counters knew how much extra money the company made from these bundles, compared to a simpler scheme, and it was a lot. So the confusion stayed.
  5. The problem is cultural. For example, Google historically made its reputation by offering new features constantly. Everything about the culture was geared towards change and innovation. When they started making enterprise products, that cultural became baggage.

But none of that keeps the employees from complaining.

I can’t understand the solution of LeetCode. Can I recite and write from my memory them to achieve the effect of learning?

That’s perhaps the first stage of learning, recitation.

Using the four-stage model of learning that goes

  1. Unconscious Incompetence
  2. Conscious Incompetence
  3. Conscious Competence
  4. Unconscious Competence

that’s maybe a 2 to 2.5 there. You know you haven’t really understood why you are doing things that way and without detailed step-by-step, you don’t yet know how you would design those solutions.

You need to step back a bit, by reviewing some working solutions and then using those as examples of fundamentals. That might mean observing that there is a for() loop, for example – why? What is it there for? How does it work? What would happen if you changed it? If you wanted to use a for loop to write out “hello!” 8 times, how would you code that?

As you build up the knowledge of these fundamental steps, you’ll be able to see why they were strung together the way they were.

Next, practice solving smaller challenges. Use each of these tiny steps to create a solution – one where you understand why you chose the pieces you chose, what part of the problem it solves and how.

As a software engineering hiring manager, would you be concerned if the candidate who has applied for a position has changed 3 jobs in 4 years?

Early 2020 has been a very rough period for many companies who laid off tons of good people, many of which have bounced to a company who was not a good fit and eventually went to a third one. Forced remote work was also difficult for many folks. So in the current context, having changed 3 jobs in the last 4 years is really a non-event.

Now more generally, would my hiring recommendation be influenced by a candidate having changed jobs several times in a short period of time?

The assumption here is that if a candidate has switched jobs 3 times in 4 years, there must be something wrong.

I think this is a very dangerous assumption. There are lots of things that cause people to change jobs, sometimes choice, sometimes circumstances, and they don’t necessarily indicate anything wrong in the candidate. However, what could be wrong in a candidate can be assessed in the interview, such as:

  • is the candidate respectful? Is the candidate able to disagree consrtuctively?
  • does the candidate collaborate?
  • Does the candidate naturally support others?
  • Has the candidate experience navigating difficult human situations?
  • etc, etc.

There are a lot of signals we can detect in the interview and we can act upon them. Everything that comes outside of the interview / outside of reference check is just bias and should be ignored.

The hiring decision should be evidence-based.

What does it feel like to have an IQ of 140?

My IQ was around 145 the last time I checked (I’m 19).

I feel lots of gratitude for my ability to deeply understand and comprehend ideas and concepts, but it has definitely had its “downsides” throughout my life. I tend to think very deeply about things that I find interesting and this overwhelming desire to understand the world has led me to some dark places. When I was around 9 or 10, I discovered the feeling of existential panic. I had watched an astronomy documentary with my father (who is a geoscience professor) and was completely overwhelmed with the fact that I was living on an unprotected orb, orbiting around a star at speeds far faster than I could even comprehend. I don’t think anyone in my family expected me to really grasp what the documentary was saying so they were a bit alarmed when I spent that whole night and most of the next week panicking and hyperventilating in my bedroom.

I lost my mom to suicide when I was 11 which sent me into a deep depression for several years. I found myself thinking a lot about death and the meaning of human existence in my earlier teenage years. I was really unmotivated to do school work all throughout high school because I found no meaning in it. I didn’t understand why I was alive, or what being alive meant, or if there even was any true meaning to life. I constantly struggled to see how any of it truly mattered in the long run. What was the point of going to the grocery store or hanging out with my friends or getting a drivers license? I was an overdeveloped primate forced to live in and contribute to a social group that I didn’t ask to be in. I was living in a strange universe that made no sense and I was being expected to sit at a desk for 8 hours every day? Surrounded by people who didn’t care about anything except clothing and football games? No way man, count me out. I spent a lot of nights just sitting in my bedroom wondering if anything I did really mattered. Death is inevitable and the whole universe will one day end, what’s the point. I frequently wondered if non-existence was inherently better than existence because of all of the suffering that goes hand in hand with being a conscious being. I didn’t understand how anyone could enjoy playing along in this complex game if they knew they were all going to die eventually.

Heavy stuff, yeah.

When I was 18 I suddenly experienced what some people label as an “ego death” or a “spiritual awakening” in which it suddenly occurred to me that the inevitably of death doesn’t mean that life itself is inherently meaningless. I realized that all of my actions affect the universe and I have the ability to set off chain reactions that will continue to alter the world long after I’m gone. I also realized that even if life is inherently meaningless, then that is all the more reason to enjoy being alive and to experience the beauty and wonder of the world while I’m still around. After that day I began meditating daily to achieve a deeper awareness of myself and try to find inner peace. I began living for the experience of being alive and nothing else. All of this has brought me great peace and has allowed me to enjoy learning again. For so long learning was terrifying to me because it meant that I was going understand new information that could potentially terrify me. Information that I could not unlearn. I have become a very emotionally sensitive person after the death of my mother, so I simply could not handle the weight of learning about existential concepts for a while. Now that I’ve been able to find a state of peace within myself and radically accept the fact that I will die one day (and that I do not know what occurs after death) I have begun to enjoy learning again! I read a lot of nonfiction and fiction alike. I enjoy traveling and seeing the world from as many different perspectives as possible. Talking to new people and attempting to see my world through their eyes is very enjoyable for me. Picking up new skills is generally very easy for me and I spend a lot of my free time pondering philosophical issues, just because it’s fun for me. I’m not a very social person, I like having a few close friends, but I mostly enjoy being alone.

So all in all, I think having an IQ of 140+ is a very turbulent experience that can be very beautiful! When you are able to truly understand deep concepts, it can seriously freak you out, especially when you’re searching for meaning and answers to philosophical problems. If I hadn’t embraced a way of life that revolves around radically acceptance, I don’t think I would have the guts to look as deeply into some things as I do. However, since I do have that safety cushion, I’m able to shape my perception of the world with the knowledge that I learn. This allows me to see incredible beauty in our world and not take things too personally. When I have a rough day, all I need to do is sit on my roof for half an hour and look at the stars. It reminds me that I am a very small animal in a very big place that I know very little about. It really puts all of my silly human problems in perspective.

If no-code is the future, is a CS major even worth it?

If you can explain to me how “no-code is the future”, maybe there’s a useful response to this.

As far as I can tell, “no-code” means that somebody already coded a generic solution and the “no-code” part is just adapting the generic solution for a specific problem.

Somebody had to code the generic solution.

As to the second part, “is a CS major even worth it?” I’ve had a 30+ year career in software engineering, and I didn’t major in CS. That hasn’t kept me from learning CS concepts, it hasn’t kept me from delivering good software, and it hasn’t stopped me from getting software jobs.

Is a CS major even worth it? Only the student knows the answer to that.

How can we solve the issue of English speakers advantage in software programming and computer related fields over other languages speakers, considering the fact that programming languages are mostly English based?

IT’S NOT ABOUT THE PROGRAMMING LANGUAGE:

People have written no-English versions of many programming languages – but they aren’t used as much as you’d think because it’s just not that useful.

Consider the C language – there are no such English words as “int”, “bool”, ”enum”, “struct”, “typedef”, “extern”, or “const”. The words “auto”, “float” and “char” are English words – but with completely different meanings to how they are used in C.

This is the complete list of C “reserved words” – things you’d have to essentially memorize if you’re a non-English speaker…

auto, else, long, switch, break, enum, register, typedef, case, extern, return, union, char, float, short, unsigned, const, for, signed, void, continue, goto, sizeof, volatile, default, if, static, while, do, int, struct, double

…but very few of those words are used in their usual English meanings…and you have to just know what things like “union” mean – even if you’re a native english speaker.

But if you really think there is an advantage to this being your native language then:

#define changer switch

#define compteur register

#define raccord union

…and so on – and now all of your reserved words are in French.

I don’t think it’s going to help much.

IT”S ABOUT LIBRARIES AND DOCUMENTATION:

The problem isn’t something like the C language – we could easily provide translations for the 30 or so reserved words in 50 languages and have a #pragma or a command to the compiler to tell it which language to use.

No problem – easy stuff.

However, libraries are a much bigger problem.

Consider OpenGL – it has 250 named function, and hundreds of #defined tokens.

glBindVertexArray would be glLierTableauDeSommets or something. Making versions of OpenGL for 50 languages would be a hell of a lot more painful.

Then, someone has to write documentation for all of that in all of those languages.

But a program written and compiled against French OpenGL wouldn’t link to a library written in English – which would be a total nightmare.

Worse still, I’ve worked on teams where there were a dozen US programmers, two dozen Russians and a half dozen Ukrainians – spread over two continents – all using their own languages ON THE SAME PIECE OF SOFTWARE.

Without some kind of control – we’d have a random mix of variable and function names in the three languages.

So the rule was WE PROGRAM IN ENGLISH.

But that didn’t stop people from writing comments and documentation in Russian or Ukranian.

SO WHAT IS THE SOLUTION?

I don’t think there actually is a good solution for this…picking one human language for programmers to converse in seems to be the best solution – and the one we have.

So which language should that be?

Well according to:List of languages by total number of speakers – Wikipediahttps://en.wikipedia.org/wiki/List_of_languages_by_total_number_of_speakers

There are 1.3 billion English speakers, 1.1 billion Mandarin speakers, 600 million Hindi speakers, 450 Spanish speakers…and no other language gets over half of that.

So if you have to pick a single language to standardize on – it’s going to be English.

Those who argue that Mandarin should be the choice need to understand that typing Mandarin on any reasonable kind of keyboard was essentially impossible until 1976 (!!) by which time using English-based programming languages was standard. Too late!

SO – ENGLISH IT IS…KINDA.

Even though we seem to have settled on English the problems are not yet over.

British English or US English – or some other dialect?

As a graphics engineer, it took me the best part of a decade to break the habit of spelling “colour” rather than “color” – and although the programming languages out there don’t use that particular word – the OpenGL and Direct3D libraries do – and they use the US English spelling rather than the one that people from England use in “English”.

ARE PROGRAMMERS UNIQUE IN THIS?

No – we have people like airline pilots, ships’ captains.

ICAO (International Civil Aviation Organization), require all pilots to have attained ICAO “Level 4” English ability. In effect, this means that all pilots that fly international routes must speak, read, write, and understand English fluently.

However, that’s not what happened for ships. In 1983 a group of linguists and shipping experts created “Seaspeak”. Most words are still in English – but the grammar is entirely synthetic. In 1988, the International Maritime Organization (IMO) made Seaspeak the official language of the seas.

As a software engineer would you join a well established & reputed tech company although work is less interesting or would you join a startup where the work is more exciting given the compensation for both the positions are comparable?

Here’s the thing. The compensation will never be comparable.

When you join a big tech, public company, all of your compensation is public. Also it’s relatively easy to get a fair estimate of what comp looks like a few years down the road.

When you join a private company, the comp is a bet on a successful exit.

In 2015, Zenefits was a super hot company. Zoom had been around for.4 years and was very confidential.

In a now infamous Quora question[1] a user asked wether they should take an offer at Zenefits or Uber. As a result, The Zenefits CEO rescinded their offer. But most people would have chosen an offer at Zenefits or Uber, whose IPO was the most anticipated back then, over one at Zoom.

And yet Zenefits failed spectacularly, Uber’s IPO was lackluster, while Zoom went beyond all expectations.

So this is mostly about to risk aversion. Going to a large co means a “golden resume” that will always get you interviews, so it has a lot of long term value.

Working in a large company has other benefits. Processes are usually much better and there’s a lot to learn. This is also the opportunity to work on some problems at a huge scale. No one has billions of users outside of Google, Meta, Apple or Microsoft.

But working in a small private company whose valuation explodes is the only way for a software engineer to become very wealthy. The thing is though that it’s impossible for an aspiring employee to tell which company is going to experience that growth versus fail.

Footnotes[1] What is the better way to start my career, Uber or Zenefits?

What are the pros and cons to consider when quitting a job?

Originally Answered: What are the pros and cons of quitting a job?

The pro’s and con’s really depend on the specific situation.

(1) When quitting for a new position…

Pros:

  • Better pay & benefits
  • More promotion opportunities
  • New location
  • New challenges (old job may have been boring)
  • New job aligned to your interests.

Cons:

  • New job/company was seriously misrepresented
  • “New boss same as the old boss” (no company is perfect!)
  • You might have wanted a new challenge, but you are now over your head.

Note: if you have a job and are not desperate, please do your homework and remember you are also interviewing them! You want a better job in most cases (unless that moving thing is going on).

(2) When quitting over a conflict…

Pros:

  • Can sleep at night (providing it was a ethical issue and you were in the right)
    • You showed them who is the boss!
    • Plus, you wont be on the local news if they get sued, or the IRS does a audit.
  • Again, if it was a toxic environment that you get to live as opposed to a stroke on the job! No job is worth it that is impacting your health, including mental health.

Cons:

  • No unemployment in most states if you just up and quit.
  • Job search with no income puts a lot of pressure at some point to take any job
    • the good news though, is you can continue looking while earning a paycheck (and hopefully still growing skills & experience)

The reason so many people are quitting now…

Note there is a third category, when you quit due to a lifestyle change. In this case, we are looking a women quitting to be a full-time mother, or someone going back to school. A spouse getting promoted but with a move might also place the other mate in this position…

Pro:

  • You get to live the life you want.
  • You are preparing for a better career

Con:

  • Loss of income
  • Reduced social interaction (for the full-time mom)

Note here that most couples that decide to do the stay at home mom generally plan ahead so one income will cover their expenses.

Second, I also don’t consider serious health issues when you leave the work force in general to fall under the scope of this discussion.

Is practicing 500 programming questions on LeetCode, HackerEarth, etc, enough to prepare for a Google interview?

Originally Answered: Is practicing 500 programming questions on LeetCode, HackerEarth, etc enough to prepare for Google interview?

If you have 6 months to prepare for the interview I would definitely suggest the following things assuming that you have a formal CS degree and/or you have software development experience in some company:

Step 1 (Books/Courses for good understanding)

Go through a good data structure or algorithms book and revise all the topics like hash tables, arrays and strings, trees, graphs, tries, bit hacks, stacks, queues, sorting, recursion, and dynamic programming. Some good books according to me are:

The Algorithm Design Manual: Steven S Skiena: 9781848000698: Amazon.com: Books

Algorithms (4th Edition): Robert Sedgewick, Kevin Wayne: 8601400041420: Amazon.com: Books

Introduction to Algorithms, 3rd Edition (MIT Press): Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein: 9780262033848: Amazon.com: Books

Data Structures and Algorithms in Java (2nd Edition): Robert Lafore: 0752063324530: Amazon.com: Books

There are other books as well and you can use any good book which you are comfortable with.

Some good courses to take on this topic if you need a more thorough understanding: (since you have 6 months time)

Algorithms, Part I – Princeton University | Coursera

Algorithms, Part II – Princeton University | Coursera

The Stanford Coursera algorithms courses are also very good and you can look at them if you have time. It’s a bit more theoretical though.

Step 2 (Programming practice for algorithms and data structures)

Once you are done with Step 1 you need a lot of practice. It need not be a set number of problems like 500 or 1000. The best way to practice problems is to mimic an interview setting and time yourself for half an hour and solve a problem without any distraction. The steps here are to read a problem, think of a brute force solution that works very quickly, and then think of an optimized version that works and then write clean working code and come up with test cases within half an hour. Most of the top companies ask you 1 or 2 medium problems or 1 hard problem in 45 mts to 1 hour. Once you are done solving the problem you can compare your solution with the actual solution and see if there is scope to improve your solution or learn from the actual solution.

If you do the math it takes half an hour to solve a problem and at least 15 mts to look and compare with the correct solution. So 500 problems take 500 * 45 mts = 375 hours. Even if you spend 5 solid hours a day for problem-solving it comes to 75 days (2.5 months). If you are in a full-time job it’s hard to spend so much time every single day. Realistically if you spend 2–3 hours a day we are talking about 5 months just for practicing 500 problems. In my opinion, you don’t need to solve so many problems to crack the interview. All you need is a few problems in each topic and understand the fundamentals really well. The different topics for algo and ds are:

arrays and strings, bit hacks, dynamic programming, graphs, hash tables, linked lists, math problems, priority queues, queues, recursion, sorting, stacks, trees, and tries. As a starter try to solve 4–5 problems in each topic after you finish step 1 and then if you have time solve 2–3 problems a day for fun in each topic and you should be good. Also, it is far better to solve 5 problems than to read 50 problems. In fact, trying to cover problems by reading problems is not going to be of any use.

Step 3 (this can be done in parallel with step 1) (Systems Design)

Practice problems in systems, design (distributed systems, concurrency, OO design). These questions are common in Google and other top companies. The best way to crack this section is to actually do complex systems projects at work or school projects. There are lots of resources online which are very good for preparation for this topic.

Edit: Since I have received some request to point some resources I am listing some of my favorite ones:

Data Manipulation at Scale: Systems and Algorithms – University of Washington | Coursera

HiredInTech’s Training Camp for Coding Interviews

Eventually Consistent – Revisited

Puncsky CS Notebook – Blog

Consistent Hashing

Tutorial: Design and implementation of a simple Twitter clone using PHP and the Redis key-value store

NoSQL Patterns

Step 4 (behavioral and resume)

Please know your resume in and out and make sure you can explain all the projects mentioned in the resume. You should be able to dive as deep as needed (technically) for the projects mentioned. Also do enough research about the company you are interviewing, the product, engineering culture and have good questions to ask them

Step 5 (mock interviews)

Last but not least please make sure you have some good friends working in a good company or your classmate mock interview you. You also have several resources online for this service. Also, work on the feedback you get from the mock interview. You can also interview a few companies you are not interested to work as a practice interview before your goal companies.

I already know DSA and can solve 40%-50% LeetCode easy problems. Is it possible for me to be prepared for a Google coding interview in the next 2-3 months? If it’s possible, then how?

It is possible for some people; I don’t know whether it is possible for you.

You’re solving 50% of easy problems. Reality check: that’s…cute. Your target success rate, to have a good chance, should be near-100% on Easy, 75% on Medium, and 50% on Hard. On top of that, non-Leetcode rounds like system design should be solid, too.

You can see there’s a big gap between where you are and where you need to be.

The good news is that despite how large that gap is, without a doubt, there have been cases of people being able to learn fast enough to cover that gap in 90 days. These cases are not at all common, and I will warn you that the vast majority of people who are where you are now cannot get to where you need to be in 90 days. So, the odds are against you, but you might be better than the odds would say.

What is special about the situations of the people who can get there that fast? Off the top of my head, the key factors are:

  • A strong previous background in CS and algorithms
  • Being able to spend a significant amount of time daily to study
  • High aptitude / talent / intelligence for learning these sorts of concepts
  • Having an effective methodology for learning. The fact that you’re actively solving problems on Leetcode is a decent start here.

If the above factors describe you, you might be better off than the odds would suggest. It is at least possible that you could achieve your goal.

Good luck and happy job hunting!

I have heard I need to spend at least 1000 hours to prepare for the Google or Facebook interview. Is it true?

(Note: I’ve interviewed hundreds of developers in my time at Facebook, Microsoft and now as the co-founder and CEO of Educative. I’ve also failed several coding interviews because I wasn’t prepared. At Educative, we’ve helped thousands of developers level up their careers with hands-on courses on programming languages, system design, and interview prep.)

Is Interview Prep a Full-time Job?

Let’s break it down. A full-time job – 40 hours per week, 52 weeks per year – encompasses 2080 hours. If you take two weeks of vacation, you’re actually working 2,000 hours. The 1,000 hours recommendation is saying you need six months of full-time work to prepare for your interview at a top tech company. Really?

I think three months is a reasonable timeframe to fully prepare. And if you’ve interviewed more recently, studying the specific process of the company where you’re applying can cut that time down to 4-6 weeks of dedicated prep.

I’ve written more about the ideal interview prep roadmap for DEV Community, but I’ll give you the breakdown here.

The “Secret” to a Successful Interview Prep Plan

First of all, I want to be clear that there’s no silver bullet to interview prep. But during my time interviewing candidates at Facebook and Microsoft, I noticed there was one trait that all the best candidates shared: they understood why companies asked the questions they did.

The key to a successful interview prep program is to understand what each question is actually trying to accomplish. Understanding the intent behind every step of the interview process helps you prepare in the right way.

A lot of younger developers think they need to be experts in a few programming languages, or even just one language in order to crack the developer interview. Writing efficient code is a crucial skill, but what software companies are actually looking for (especially the big ones with custom libraries and technology stacks that you will be expected to learn anyway) is an understanding of the various components of engineering, as well as your creative problem-solving ability.

That breaks down into five key areas that “Big Tech” companies are focused on in the interview process:

1. Coding

Interviewers are testing the basics of your ability to code. What language should you be using? Start with the language you know best. Especially in larger companies, new syntaxes can be taught or libraries used if you establish you can execute well. I have interviewed people that used programming languages that I barely know myself. I know C++ inside and out, so even though Python is a more efficient language, I would always personally choose to interview using C++. The most important thing is just to brush up on the basics of your favorite programming language.

The questions in coding interviews focus on generic problem-solving, data structures (Mastering Data Structures: An interview refresher), and algorithms. So revisit concepts that you haven’t touched since undergrad to have a fresh, foundational understanding of topics like complexity analysis (Algorithms and Complexity Analysis: An interview refresher), arrays, queues, trees, tries, hash tables, sorting, and searching. Then practice solving problems using these concepts in the programming language you have chosen.

Coding Interview Preparation | Codinginterview has gathered hundreds of real coding questions asked by top tech companies to get you started.

2. OS and Concurrency Concepts

Whether you’re building a mobile app or web-scale systems, it’s important to understand threads, locks, synchronization, and multi-threading. These concepts are some of the most challenging and factor heavily into your “hiring level” at many organizations. The more expert you are at concurrency, the higher your level, and the better the pay.

Since you’ve already determined the language you’re using in (1), study up on process handling using that same language. Prepare for an interview – Concurrency

3. System Design

Like concurrency problems, system design is now key to the hiring process at most companies, and has an impact on your hiring level.

System Design Interviews (SDIs) are challenging for a couple reasons:

  • There isn’t a clear-cut answer to an open-ended question where a candidate must work their way to an efficient, meaningful solution to a general problem with multiple parts.
  • Most candidates don’t have a background designing large-scale systems in the first place, as reaching that level is several years into a career path and most systems are designed collaboratively anyway.

For this reason, it is important to spend time clarifying the product and system scope, a quick back-of-the-envelop estimation, defining APIs to address each feature in the system scope and defining the data model. Once this foundational work is done, you can take the data model and features to actually design the system.

If that seems like a daunting task, you can brush up on a few major APIs for free on Educative or dig deeper with our Scalability & System Design learning path, which includes the Grokking the System Design Interview course.

4. Object-Oriented Design

In Object-Oriented Design questions, interviewers are looking for your understanding of design patterns and your ability to transform the requirements into comprehensible classes. You spend most of your time explaining the various components, their interfaces and how different components interact with each other using the interfaces. Interviewers are looking for your ability to identify patterns and to apply effective, time-tested solutions rather than re-inventing the wheel. In a way, it is the partner of the system design interview.

Object-oriented programming deals with bundling certain properties with a specific object, and defining those objects according to its class. From there, you deal with encapsulation, abstraction, inheritance, and polymorphism. [Object-Oriented Basics – Grokking the Object Oriented Design Interview (educative.io)]

5. Cultural Fit

This is the one that doesn’t have a clear cut learning path, and because of that, it is often overlooked by developers. But for established companies like Google and Amazon, culture is one of the biggest factors. The skills you demonstrate in coding and design interviews prove that you know programming. But without the right attitude, are you open to learning? Are you passionate about the product and want to build things with the team? If not, companies can think you’re not worth hiring. No organization wants to create a toxic work environment.

Since every company has a few different distinguishing features in their culture, it’s important to read up on what their values and products are (Coding Interview Preparation | Codinginterview has information on many top tech companies, including Google and Facebook). Then enter the interview track ready to answer these basics:

  • Interest in the product, and demonstrate understanding of the business. (Don’t mistake Facebook’s business model, which relies on big data, for AWS or Azure, which facilitate big data as a service. If you’re going into Google, know how user data and personalization is the core of Google’s monetization for its various products and services, while knowing what makes Android unique compared to iOS. Be an advocate.)
  • Be prepared to talk about disagreements in the workplace. If you’ve been working for more than a few years, you’ve had disagreements. Even if you’re coming out of school, group projects apply. Companies want to know how you work on a team and navigate conflict.
  • Talk about how the company helps you build and execute your own goals both as a technologist and in your career. What are you passionate about?
  • Talk about significant engineering accomplishments – what have you built; what crazy/difficult bugs have you solved?

Conclusion

Strategic interview prep is essential if you want to present yourself as the best candidate for an engineering role.

It doesn’t have to take 1,000 hours, nor should it – but at big companies like Google and Facebook where the interview process is so intentional, it will absolutely benefit you to study that process and fully understand the why behind each step.

There are plenty of battle-tested resources linked in my answer that will guide you throughout the prep process, and I hope they can be helpful to you on your career journey.

Happy learning!

I have practiced over 300 algorithms questions on LintCode and LeetCode. I have been unemployed for almost 9 months and I got 8 interviews and all failed in the coding test. I still can’t get any offer. What should I do?

Originally Answered: I have practiced over 300 algorithms questions on LintCode and LeetCode but still can’t get any offer, what should I do?

I have interviewed and been interviewed a number of times, and I have found out that most of the time people (including myself) flunk an interview due to the following reasons:

  1. Failing to come up with a solution to a problem:
    If you can’t come up with even one single solution to a problem, then it’s definitely a red flag since that reflects poorly on your problem solving skills. Also, don’t be afraid to provide a non-optimal solution initially. A non-optimal solution is better than no solution at all.
  2. Coming up with solutions but can’t implement them:
    That means you need to work more on your implementation skills. Write lots and lots of code, and make sure you use a whiteboard or pen and paper to mimic the interview experience as much as possible. In an interview you won’t have an IDE with autocomplete and syntax highlighting to help you. Also make sure that you’re very comfortable in your programming language of choice.
  3. Solving the problem but not optimally:
    That could mean that you’re missing some fundamental knowledge of data structures and algorithms, so make sure that you know your basics well.
  4. Solving the problem but after a long time, or after receiving too many hints:
    Again, you need more problem solving practice.
  5. Solving the problem but with many bugs:
    You need to properly test your code after writing it. Don’t wait for the interviewer to point out the bugs for you. You wouldn’t want to hire someone who doesn’t test their code, right?
  6. Failing to ask the interviewer enough questions before diving into the code:
    Diving right into the code without asking the interviewer enough questions is definitely a red flag, even if you came up with a good solution. It tells the interviewer that either you’re arrogant, or that you’re reckless. It’s also not in your favor, because you may end up solving the wrong problem. Discussing the problem and asking questions to the interviewer is important because it ensures that both of you are on the same page. The interviewer’s answers to your questions may also provide with some very useful hints that may greatly simplify the problem.
  7. Being arrogant:
    If you’re perceived as arrogant, no one will want to hire you no matter how good you are.
  8. Lying on the resume:
    Falsely claiming knowledge of something, or lying about employment history is a huge red flag. It shows dishonesty, and no one wants to work with someone who is dishonest.

I hope this helps, and good luck with your future interviews.

How often do tech companies ask LeetCode Hard questions during interviews?

Unless we’re talking about Google, which has problems that are unique to them in comparison to the rest, you can be sure that big tech companies ask LeetCode-style questions quite often. Seeing LeetCode Hard problems specifically, however, is not that common in these interviews, and it’s more likely that you’ll be facing LeetCode Medium questions and one or two Hard questions at best. This is because having a time limit to solve them as well as an interviewer right beside you already adds enough pressure to make these questions feel harder than they normally would be; increasing their difficulty would simply be detrimental to the interviewing process.

I suggest that you avoid using the difficulty of LeetCode questions that you can solve as a way of telling if you’re prepared for your interviews as well because it can be pretty misleading. One reason this is the case is that LeetCode’s environment is different from an interviewing environment; LeetCode cares more about running time and the optimal solution to a problem, while an interviewer cares more about your approach to the question (an intuitive solution can always be optimized further with a discussion between you and the interviewer).

Another reason you should avoid worrying too much about LeetCode-style questions is that FAANG companies are starting to refrain from asking them, as they’re noticing that many candidates come to their interviews already knowing the answer to some of their questions; currently, if your interviewer notices that you already know the answer to the question you’re given, they won’t take it into account and instead will move on to another question, as already knowing how to solve the problem tells them nothing about the way you approach challenging situations in the first place.

Also, you should consider that LeetCode only lets you practice what you already know in coding; if you don’t have a good knowledge of data structures & algorithms beforehand, LeetCode will be a difficult resource to use efficiently, and it also won’t teach you anything about important non-technical skills like communication skills, which is a crucial aspect that interviewers also evaluate. Therefore, I also suggest that you avoid using LeetCode as your only resource to prepare for your technical interviews, as it doesn’t cover everything that you need to learn on its own.

For example, you may want to enroll in a program like Tech Interview Pro as you use LeetCode. TIP is a program that was created by an ex-Google software engineer and was designed to be a “how to get into big tech” course, with over 20 hours of instructional video content on data structures & algorithms and system design.

Another good resource that you could use, this time to cover the behavioral aspect of interviews, is Interviewing.io. With it, you can engage in mock interviews with other software engineers that have worked with Facebook and Google before and also receive feedback on your performance.

You could also read a book like Cracking the Coding Interview, which offers plenty of programming questions that are very similar to what you can expect from FAANG companies, as well as valuable insight into the interviewing process.

Best of luck with your interviews!

Are technical internships like Google, Amazon, and Facebook more selective than getting into Harvard?

Harvard is seen in popular culture as being very selective, and so any funnel which has a conversion rate lower than 5% is going to describe itself as “more selective than Harvard”. “More selective than Harvard” has 70m hits on Google. When Walmart opened a DC store, it hired about 2.5% of the people that sent applications, and ran a story that it was “twice as selective as Harvard”. Tech internships, somewhat unsurprisingly, are harder to get as jobs at Walmart.

Generally speaking, the more LeetCode problems you solve, the better your odds of getting an offer will be. Be careful, however, as using the number of problems you solve on LeetCode as a reference for how ready you are for your technical interviews is misleading, especially if it’s for Google and Facebook. Even if you solve every problem on LeetCode (please don’t try this), there’s still a chance you won’t get an offer, and there are several reasons why.

First of all, coding is not the only thing taken into consideration by interviewers from big tech companies. One of the main things they look for in a candidate is the presence of strong soft skills like teamwork, leadership, and communication. If you’re raising red flags in that department—if the interviewer doesn’t think you have the leadership skills to lead a team down the road, for example—odds are that you’re going to get overlooked. They also expect you’ll be able to clearly explain your thought process before solving a given coding problem, which is something a surprising number of developers have trouble with.

The second problem with using LeetCode alone is that it can only help you practice data structures & algorithms and system design, but not exactly teach you about them. This might not be an issue if you’re solving questions from the Easy section of LeetCode, but once you get to the Medium and Hard problem sets, you’ll need more theoretical knowledge to properly handle these problems.

So, ideally, you’ll want to prepare using resources that help you learn more about DS&A and systems design before you start practicing on LeetCode, and you’ll also want to work on your behavioral skills to ensure you do well there, too. Here are some tools that can help:

  • Interviewing.io: A site where you can engage in mock interviews with other software engineers—some of whom have worked at Google and Facebook—and receive immediate, objective feedback on your performance.
  • Tech Interview Pro: An interview prep program designed by a former Google software engineer that includes 150+ instructional video lessons on data structures & algorithms, systems design, and the interview process as a whole. TIP members also get access to a private Facebook group of 1,500+ course graduates who’ve used what they learned in the course to land jobs at Google, Facebook, and other big tech companies.
  • Educative’s Scalability & System Design for Developers Course: An introductory systems design course that will teach you how to think about architecture trade-offs and design systems at scale for enterprise-level software.

So, using LeetCode on its own would prepare you well for questions about data structures & algorithms, but may leave you unprepared for questions related to systems design and the behavioral aspect of your interviews. But by complementing LeetCode with other resources, you’ll put yourself in a much better position to receive an offer from Google, Facebook, or anyone else. Best of luck.

Dmitry Aliev is correct that this was introduced into the language before references.

I’ll take this question as an excuse to add a bit more color to this.

C++ evolved from C via an early dialect called “C with Classes”, which was initially implemented with Cpre, a fancy “preprocessor” targeting C that didn’t fully parse the “C with Classes” language. What it did was add an implicit this pointer parameter to member functions. E.g.:

  • struct S { 
  • int f(); 
  • }; 

was translated to something like:

  • int f__1S(S *this); 

(the funny name f__1S is just an example of a possible “mangling” of the name of S::f, which allows traditional linkers to deal with the richer naming environment of C++).

What might comes as a surprise to the modern C++ programmer is that in that model this is an ordinary parameter variable and therefore it can be assigned to! Indeed, in the early implementations that was possible:

  • struct S { 
  • int n; 
  • S(S *other) { 
  • this = other; // Possible in C with Classes. 
  • this->n = 42; // Same as: other->n = 42; 
  • }; 

Interestingly, an idiom arose around this ability: Constructors could manage class-specific memory allocation by “assigning to this” before doing anything else in the constructor. E.g.:

  • struct S { 
  • S() { 
  • this = my_allocator(sizeof(S)); 
  • … 
  • ~S() { 
  • my_deallocator(this); 
  • this = 0; // Disabled normal destructor post-processing. 
  • … 
  • }; 

That technique (brittle as it was, particularly when dealing with derived classes) became so widespread that when C with Classes was re-implemented with a “real” compiler (Cfront), assignment to this remained valid in constructors and destructors even though this had otherwise evolved into an immutable expression. The C++ front end I maintain still has modes that accept that anachronism. See also section 17 of the old Cfront manual found here, for some fun reminiscing.

When standardization of C++ began, the core language work was handled by three working groups: Core I dealt with declarative stuff, Core II dealt with expression stuff, and Core III dealt with “new stuff” (templates and exception handling, mostly). In this context, Core II had to (among many other tasks) formalize the rules for overload resolution and the binding of this. Over time, they realized that that name binding should in fact be mostly like reference binding. Hence, in standard C++ the binding of something like:

  • struct S { 
  • int n; 
  • int f() const { 
  • return this->n; 
  • } s = { 42 }; 
  • int r = s.f(); 

is specified to be approximately like:

  • struct S { int n; } s = { 42 }; 
  • int f__1S(S const &__this) { 
  • return (&__this)->n; 
  • int r = f__1S(s); 

In other words, the expression this is now effectively a kind of alias for &__this, where __this is just a name I made up for an unnamable implicit reference parameter.

C++11 further tweaked this by introducing syntax to control the kind of reference that this is bound from. E.g.,

  • struct S { 
  • int f() const &; 
  • int g() &&; 
  • }; 

can be thought of as introducing hidden parameters as follows:

  • int f__1S(S const &__this); 
  • int g__1S(S &&__this); 

That model was relatively well-understood by the mid-to-late 1990s… but then unfortunately we forgot about it when we introduced lambda expression. Indeed, in C++11 we allowed lambda expressions to “capture” this:

  • struct S { 
  • int n; 
  • int f() { 
  • auto lm = [this]{ return this->n; }; 
  • return lm(); 
  • }; 

After that language feature was released, we started getting many reports of buggy programs that “captured” this thinking they captured the class value, when instead they really wanted to capture __this (or *this). So we scrambled to try to rectify that in C++17, but because lambdas had gotten tremendously popular we had to make a compromise. Specifically:

  • we introduced the ability to capture *this
  • we allowed [=, this] since now [this] is really a “by reference” capture of *this
  • even though [this] was now a “by reference” capture, we left in the ability to write [&, this], despite it being redundant (compatibility with earlier standards)

Our tale is not done, however. Once you write much generic C++ code you’ll probably find out that it’s really frustrating that the __this parameter cannot be made generic because it’s implicitly declared. So we (the C++ standardization committee) decided to allow that parameter to be made explicit in C++23. For example, you can write (example from the linked paper):

  • struct less_than { 
  • template <typename T, typename U> 
  • bool operator()(this less_than self, 
  • T const& lhs, U const& rhs) { 
  • return lhs < rhs; 
  • }; 

In that example, the “object parameter” (i.e., the previously hidden reference parameter __this) is now an explicit parameter and it is no longer a reference!

Here is another example (also from the paper):

  • struct X { 
  • template <typename Self> 
  • void foo(this Self&&, int); 
  • }; 
  • struct D: X {}; 
  • void ex(X& x, D& d) { 
  • x.foo(1); // Self=X& 
  • move(x).foo(2); // Self=X 
  • d.foo(3); // Self=D& 

Here:

  • the type of the object parameter is a deducible template-dependent type
  • the deduction actually allows a derived type to be found

This feature is tremendously powerful, and may well be the most significant addition by C++23 to the core language. If you’re reasonably well-versed in modern C++, I highly recommend reading that paper (P0847) — it’s fairly accessible.

When an employee is hired, there is a step in the process where they are given a stack of documents to sign that (anecdotally) I’ll venture maybe 1 in 1,000 actually read. One of the least understood (or read) is the notice that the company controls, collects and analyzes all communications, internet activity and data stored on company-owned or -managed devices and systems.

This includes network traffic that flows across their servers. It’s safe to assume that mid-to-large employers are fully aware of the amount of on-the-clock time employees spend shopping, tweeting or watching YouTube, and know which employees are spending inordinate amounts of ‘company time’ shopping on Amazon rather than tackling assignments.

This also include Bring Your Own Device policies— where employees are allowed to use their personal smartphone, tablet or laptop for business purposes. Companies don’t always ‘exploit’ the policy for nefarious surveillance purposes, but employers are within their rights to collect information like location data from your BYOD smartphone both on and off the clock.

An example of where this can hurt employees is when they start to look for another job.

If you email/Slack/message your supervisor and ask for a personal day off to attend to a family matter, but your device logs show you are accessing job-search sites and your location data suggests your aren’t at home or even within the radius of a competitor’s office, they know. This tends to make your boss cranky, and can adversely impact your employment to the point of losing your job.

I disagree with this kind of intrusive surveillance, and the presumption of guilt employees face when they take steps to protect themselves by using encrypted tools like Signal, proxy servers or switching devices to Airplane Mode intrudes on the employee’s legitimate rights to privacy: you may not want your employer to know that you’re seeing a psychiatrist on your lunch hour, and they really have no reasonable expectation for you to disclose this (or not take steps to conceal it.)



 

Facebook recruiting breaking poaching agreements really lead to measurable higher salaries at Google?

I think so. I remember there was a noticeable number of people going to Facebook, and some discussion of it among the employees. And then there was an explicit event where Google rearranged its compensation strategy. Everyone got a huge raise just at that moment, and from that point on the salaries and stock grants became close to the top of the market, as they need to be for a company that hires top talent.

I have no internships. I just graduated with a degree in CS. How can I get a job at FAANG?

If you can’t get FAANG to pay attention to you, you probably need to get another job first. Perhaps one of the companies that are considered to be pretty good would be interested.

It is actually quite hard to get an entry-level role at a top tech company, because where you went to college (and internships, which you don’t have) plays a disproportionate role. It’s not surprising, because what else can they go on? Interviewing is expensive, and there are hundreds of applicants per opening, so they want to pre-filter candidates somehow.

Once you have a few years of experience, things look a little better, especially if you climb up the prestige pole. For instance, Microsoft (or Twitter where I work today) isn’t FAANG, but you can be sure that recruiters would take applicants from there seriously, and you would have a good chance to get an interview. But the main factor is what you manage to do in your time at work. If you do well, get promoted, demonstrate clear impact (that you can articulate externally), build your professional network, that would improve your chances to both get your foot in the door, and also to pass the interviews.

There are also other things you can do, but I think they depend on luck too much. Slowly improving your portfolio is the way to go, I think.

What’s the best future web programming language to work in a big company like Google, Facebook, and Microsoft?

All of these companies assume that if you know the front-end domain, you can learn whatever technology du jour to become a front-end developer, and besides, if you don’t know anything about front-end, you can still grow into a front-end developer if that’s the path you’re interested in.

That being said, TypeScript is increasingly becoming the standard way to write client-side web code. Both Microsoft and Google are very committed to TS, while Facebook uses JavaScript with Flow. Google also uses Dart for some of its front end.

Likewise, there are a number of technologies on which the larger companies have taken diverging choices. Google is very committed to gRPC, I mean, g stands for Google; while Facebook is behind graphQL. (graph being, originally. the “social graph” of Facebook). AFAIK, Microsoft uses both.

Neither Google nor Facebook have ever really embraced node.js. This would have seemed odd a few years ago but now the web ecosystem is generally turning away from tools and web servers written in node.js. I don’t know for sure what Microsoft uses for its web servers.

Facebook is unsurprisingly very committed to React and React Native. Google though uses a number of web frameworks, including non-open sourced ones, and among others Angular and Flutter. Microsoft, AFAIK, uses React and React Native and Angular.

But all these skills are transferable. If you understand React, it’s easy to learn Angular and conversely; TypeScript and Flow have similarities, etc.

One common denominator is HTML, CSS, web APIs and web standards, which are always relevant.

Is 40 too old to apply for an SDE role in FAANG?

Not at all, I applied for a role with Google the month before my 52nd birthday.

Nobody ever asked me during the application and interview process, “Can you keep up with these young kids and with new technologies?”

Doesn’t matter if you’re 22 or 52 when you join Google — during your first year you’re going to soak up knowledge like it came from a fire hose.

If that sounds interesting to you, then by all means, apply!

How can I figure out if my interviewer is impressed in an Amazon interview? My interviewers gave reactions after every answer such as wonderful, very good, I love it. Is this usual?

Your goal, in an interview, is not to impress your interviewer, but to demonstrate that you have the necessary skill set to be hired.

In a large tech company, the threshold to be considered “impressive” is pretty high… you have people that had superlative achievements in their field (or outside of tech), and in their day to day they’re just treated like normal people. I never interviewed for Amazon, but I interviewed (and got hired) at both Facebook and Google, and both of my interviewer brackets included folks who had their own Wikipedia entry (and since then, all of my Facebook interviewers had amazing careers and most got their own Wikipedia page). So that’s the caliber of folks that your interviewers work with on a daily basis.

So your interviewer is not going to be impressed by your interview performance. That said, I’ve observed that many tech employees treat others as if they could be the next Ada Lovelace or the next Steve Jobs no matter their current achievements. This is not forced, but it’s an attitude that comes naturally because we’ve observed so many people achieve greatness. Interviewers would love nothing more than to give the highest recommendation for the candidate that they are seeing right now, it’s very fulfilling (conversely, having to reject a candidate is always a bit frustrating). So I think it’s fair that your interviewer is hoping you can become a superstar, but that hope is the same as for every other candidate and not directly linked to how well you are doing right now.

Google’s interview process leans towards making sure that an unsuitable candidate is not hired, they are ok if a few suitable candidates are missed in the process.

There is also a factor of chance involved in the process. Here is a story to prove that:

I have personally asked at least 5 engineers at Google if they would be willing to interview again assuming they would be offered 1.5 times their current compensation. Obviously they loose the job if they don’t clear the interview. I am yet to meet somebody willing to take this bargain , I wont take it either.

Btw google also offers anybody who leaves google to comeback and join at the same level without an interview if they comeback within 2 years. My guess is that they also realize the chance involved.

Not clearing an interview at google is an indicator of only one thing, that you did not clear a google interview. Don’t draw conclusions about your ability based on this.

What laptop do FAANG software developers seem to prefer? Why?

At Google there’s a selection of laptops you can choose from: a couple of Macs, a couple of Chromebooks, a couple of Linux laptops and a couple of windows laptops. Usually there’s a smaller, lighter version, for people who favor portability, and a larger version if you prefer a larger screen.

I’ve seen developers use all. I’d guess that Macs are most common (but under 50%} and Windows machines are least common.

I use a Chromebook (well, two Chromebooks). You turn it on, you log in and it looks exactly the same as your other Chromebook. This saves me carrying a laptop between work and home. If you work from another office, you don’t need to carry your laptop, you just grab one off the shelf, log in, and it looks the same as the computer you left at home.

(I tried using a Mac, I couldn’t get used to it, I didn’t know how to do anything, the keyboard shortcuts drove me crazy and so I gave it back and got a Chromebook).

Why is employee activism seen more in Google but not in other companies like Facebook and Amazon?

Google and Meta (formerly Facebook) have a long-standing culture where employees believe that they’re hot stuff and that the company has to keep them happy because the company needs them as much as they need the company. Amazon doesn’t have that, probably because they fire people pretty often, making many of the remaining employees feel disposable.

Google and Meta have different concepts of culture fit—or at least they did historically. At Google, culture fit means “don’t be a person who’s hard to work with”. At Meta, culture fit means “be a person who believes that we are doing great things here and who will be excited to work hard on those great things”. As a result, it tends to be easy for Meta to keep convincing their existing employees that the company is doing the right thing. Google, on the other hand, ends up with a significant proportion of employees who are not easily convinced, and demand change.

Though it’s been so long since I’ve actually worked in the tech industry that I’m not sure if Meta still fits the description I gave above, and there are signs that Google has been trending away from the description I gave above.

The question was:

Why is employee activism seen more in Google but not in other companies like Facebook and Amazon?

When people who have PhDs want work in FAANG, do many of them gravitate more towards Google than any of the other FAANG companies?

Just to add a small note to Dimitriy’s great answer, computer science PhDs tend to be analytical and hyperrational. Working for Google is probably the single best “pass” to choosing whatever the hell you want for the rest of your career, or at least for the next step or two. I think some CS PhDs work for Google not because it’s what they want, but because they don’t know what they want, and if you don’t know what you want and you can get a job there, it would be hard to do better than Google. Why not make $250,000 a year while figuring out your next step? The other companies in this so-called “top-tier” have issues; they are potentially great employers, but their issues make them anywhere from slightly to dramatically less attractive.

Why is it much harder to get into trading firms and hedge funds such as Jane Street and Two Sigma than FAANG/top tier companies?

The main factor why top prop trading firms and hedge funds are difficult to get into compared to tech companies is their size.

According to Wikipedia Two Sigma has about 1600 employees[1] and Jane Street has about 1900 employees .[2] Even the largest hedge fund, Bridgewater, only has 1500[3] and the third largest hedge fund, Renaissance Technology manages $130 billion with 310 employees.

Maybe these numbers on Wikipedia aren’t exact but I’d bet they’re well within the ballpark of being accurate.

Facebook has nearly 60,000 employees ,[4] Amazon has 160,000 ,[5] Apple has 154,000,[6] Netflix has around 12,000[7], and Google has 140,000[8]. Again, maybe these number aren’t precise but I don’t feel like doing more in depth research.

However, it’s pretty obvious to see that the big tech companies employ multiples of what those finance firms do and quite simply there are far more opportunities at those tech companies. More seats mean it’s going to be less competitive to be hired.

Second, those top hedge funds and prop trading firms pay well. Like really well.

And Jane Street’s 2020 graduate hires straight from college were paid a $200k annual base salary, plus a $100k sign-on bonus, plus a $100k-$150k guaranteed performance bonus. Junior bankers’ high salaries look a little paltry by comparison.[9]

So a new college grad makes $400-$450k. That’s a 22–23 year old making that. That same article found documents that said the average per employee in their London office was $1.3 million. Some make more and some make less, but that’s an eye wateringly high number when you consider all of the admin and support aren’t making close to that.

A friend’s younger brother worked at Jane Street about 10 years ago. He may still but I haven’t talked to her much since we moved. He was a rock star at Jane Street, and while I’m relying on my memory of a 10 year old conversation so I may not be totally accurate, he was in his late 20’s or early 30’s and made $4 million (and it may actually have been $8M) that year.

I know tech people are paid well but I doubt many, if any, make $400-$450k in year one and are making millions by their late 20’s is unheard of unless they founded or join a startup at the right time.

In addition, the interview processes at those firms is insanely difficult. I’ve never worked or interviewed at them but I’ve heard war stories. Just to get your foot in the door is nearly impossible then getting an offer to work there is basically impossible

My friend’s brother was half way through an absolutely top PhD program in Physics when he was recruited by them. I don’t consider myself a slouch and I’ve met a ton of highly intelligent people, but this guy was like his brain was plugged into a computer and the internet. And he was a dynamic personality.

They hire the absolute best of the best and because they’re small and privately held they don’t actually ever need to hire or grow because the public markets can’t punish their stock price because they don’t have one. If some of those top investment firms can’t find the right fit they may simply not need to make a hire right then and can wait. They’re not big banks like Goldman that need to hire X number of analysts and associates because they need to replace the people who left.

So the main reasons that it’s tougher to get into a top hedge fund or prop trading firm than big tech is because they’re much smaller, they pay more, they are even more diligent in their hiring practices, and they hire very intelligent people.

Footnotes

[1] Two Sigma – Wikipedia

[2] Jane Street Capital – Wikipedia

[3] Bridgewater Associates – Wikipedia

[4] Number of Facebook Employees 2022/2023: Compensation, Tenure & Perks – Financesonline.com

[5] Amazon tops 1M U.S. employees

[6] Apple Inc. – Wikipedia

[7] number of nextflix employees

[8] Google – Wikipedia

[9] Jane Street paid staff $1.3m as profits soared

What would happen to Google if they lost all their source code?

If that were to happen, we’ll have bigger problems to deal with. The Google monorepo exists on tens of thousands of machines. That would mean: every data center, every workstation used by Google would suddenly be out of commission – not just turned off, but so that storage isn’t even available. This is only possible in a complete doomsday scenario.

Do FAANG developers have a hard time finding another job with higher salary given the fact FAANG salaries are top of the line?

It’s generally possible to find better compensated jobs for people with experience in big tech cos. This experience is very desirable for companies in fast growth mode – not just the technical expertise but also knowledge of processes of world-class engineering organizations. Smaller but fast-growing companies can offer better packages but with an element of risk – if the company ends up failing, the employee will only get their salary.

To Conclude:

The tech industry is booming, and there are a lot of great opportunities for those with the skills and experience to land a job at one of the FAANG companies. Google, Facebook, Amazon, Apple, Netflix, and Microsoft are all leaders in the tech industry, and they offer competitive salaries and benefits. The interview process for these companies can be intense, but if you’re prepared and knowledgeable about the company’s culture and values, you’ll have a good chance of landing the job. Perks at these companies can include free food and transportation, stock options, and generous vacation time. If you’re looking for a challenging and rewarding career in the tech industry, consider applying for a job at one of the FAANGM companies.

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I’m going to read between the lines and assume that you are working at a grade below senior at a company which is not a FAANG. I’m also assuming that you feel that you are ready and that you’ve already done the obvious, read the books, practiced questions etc.

Your senior eng interview has 3 facets, coding, system design and behavioral.

Your levers to do better at each are:

  • To get better at coding interviews, interview more candidates. Seeing what others do well and less well is very helpful. This really applies to all sorts of interviews but IMO is most helpful for coding interviews.
  • To get better at system design interviews, read more design docs at your existing company, attend more design reviews, and force yourself to participate. Comment, ask questions. It doesn’t matter if you’re off the mark. See what doesn’t make sense to you and challenge it.
  • To get better at behavioral interviews, read your perf packets and the feedback from your coworkers. Read the docs that you wrote on your career plans (If you don’t have any, ask yourself why and start one). Reflect, regularly, on what has been hardest in your career, what you have done very well, where you struggled, what you would do differently.

I’d like to answer first in general — about attrition rates in the tech sector — and then about Amazon specifically.

Industry-Wide Retention

Retention in the US high-tech industry is very challenging. I believe there are two main reasons for that.

First, there is an acute shortage of qualified workers, which means companies are desperate to get employees anywhere they can, including — sometimes mainly — by poaching them from other companies. This is why so many companies moved into the Seattle East Side in the ’90s or South Lake Union in the last five years, for example: to poach from Microsoft and Amazon, respectively.

I remember the crazy late-90’s in the Israel high-tech industry. People would come in, work for 6–12 months, then jump ship for a fancier title and a bump in pay. It was insane; it was disgusting (I mean that literally: I would sometimes feel physically sick thinking about how stupid it all was.)

The second reason — which I’m not as certain about — is that the high-tech industry is so incredibly dynamic. Things change constantly: new companies spring up and grow like crazy (Uber anyone?); “old” companies that were considered the cream of the crop a couple of years ago are suddenly untouchable (Yahoo!). New technologies explode onto the scene and old ones stagnate.

Not only does that create a lot of churn as companies keep growing and shrinking; it also creates incredible pressure on tech workers to stay on top of their game. We’re always looking for the next big technology, the next big field, then next big product… The sad part is that a lot of it is just hype, but the psychological pressure is real enough, and it makes people move around always looking for the next great opportunity.

Amazon

The reason I want to talk about Amazon — which generally suffers from the same problems I’ve described above — is that there’s a perception in the public that Amazon is somehow worse than the rest of the industry; that it has awful attrition, because it’s a terrible place to work. I’ve tackled that in a couple of other answers (e.g. this one and this one), but it’s a very persistent myth.

Much of the fault is in reports like this one from PayScale, which then get regurgitated in hundreds of stories like this one (from BuzzFeed). The basic story seems very simple: the average tenure of an Amazon employee is about a year, which is — undoubtedly — really low, even in tech-industry terms.

That’s a great example of (supposedly) Benjamin Disraeli’s famous quote, “lies, damned lies and statistics”. There are at least two reasons why this number is completely meaningless:

  • Short tenure does not mean high attrition: in the last 6–7 years the number of employees at Amazon has grown exponentially, and I mean this literally:
  • Source: Amazon: number of employees 2017 | Statista

    This means that at any time, pretty much, about 20–40% of all Amazon employees have joined less than a year ago. It’s no really surprising that they have a short tenure, is it?

    Measuring retention is not trivial, but this methodology is just plain dumb (or maybe intentionally misleading).
  • Amazon is not (only) a tech company: sure, if you compare Amazon to Google and Facebook it comes out bad. But unlike those companies, the majority of Amazon employees are not tech workers. They’re warehouse workers, drivers, customer-service people, etc. Many of them are temp workers, and many others are not considering the job as a career.

    There is a good discussion to be had about how Amazon treats these workers and whether it can do better, but it makes no sense to compare it with Microsoft or Apple; Walmart and Target would be much better comparisons.

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