Amazon data engineer interview: the only post you'll need to read

Amazon data engineer interviews are difficult. If you make it all the way to the final onsite round, you’ve still only got about a 20% chance of receiving an offer.

The good news is that the right preparation can help you beat that statistic. That’s why we’ve put together this ultimate guide: to help you maximize your chances of success.

Here’s an overview of what we’ll cover:

Let’s get started.

1. Interview process and timeline

What’s the Amazon data engineer interview process and timeline? It usually takes around one to three months and follows the steps below.

1.1 What interviews to expect

  1. Application, resume, and referrals
  2. Online assessment (in some cases)
  3. First-round interviews (1-2 calls, 45-60 min each)
  4. Onsite interviews (4-6 interviews, 45-60 min each)

Let’s take a look at these steps in more detail.

1.1.1 Application, resume, and referrals

Your first step is to get an Amazon interview.  You can apply to Amazon directly through their website, or you may have a recruiter reach out to you directly somewhere like LinkedIn.

Whether you apply or are contacted by a recruiter, it helps to have a quality and up-to-date resume that is tailored to data engineering positions, and to Amazon more specifically.

If you do have a connection to someone at Amazon, it can be really helpful to get an employee referral to the internal recruiting team, as it may increase your chances of getting into the interview process.

1.1.2 Online assessment (in some cases)

Some candidates will receive an invitation for an online test before moving on to the first-round calls. These are more common for internship and junior positions, but may appear in experienced positions as well.

This assessment will focus your technical skills, requiring a strong understanding of SQL querying and some coding. There may be a question on data modeling as well. You’ll likely have a deadline by which you have to complete the assessment, but the test itself is not timed. 

If you pass the online assessment, you’ll move on to your first calls with Amazon interviewers.

1.1.3 First-round interviews

The next step includes one or two calls with Amazon interviewers via Amazon Chime, which last 45-60 minutes each. These may take place over video or be audio-only.

The first-round interviews will include a mix of technical and behavioral questions. You may have one interview entirely focused on SQL and coding questions, followed by a second interview that focuses on behavioral questions and data modeling. Or you may have one interview that combines all of these.

You’ll typically be doing your coding using a simple text editor like LiveCode, where you’re unable to run the code or see any static analysis. While most candidates chose to code in Python, you may use a coding language of your choice.

1.1.4 Onsite interviews

Your last step in the Amazon data engineer interview process is the final onsite or virtual interview loop. This will include four to six separate rounds, which last 45-60 minutes each. 

The interviews will be one-on-ones with a mix of people from the team you’re applying to join, including data engineers, software engineers, hiring managers, and a senior executive called the Bar Raiser. 

Bar Raisers are not associated with the team you’re applying for. Instead, they focus on overall candidate quality rather than specific team needs. They get special training to make sure Amazon’s hiring standards stay high, so they are a big barrier between you and the job offer.

Expect a higher emphasis on behavioral questions at Amazon relative to other tech companies. Each interviewer is usually assigned two or three of Amazon’s 16 leadership principles to focus on during your interview. We’ll dive deeper into the questions to expect in Amazon data engineer interviews in section 2.

For more information from Amazon about these interviews, take a look at their interview prep materials.

1.2 What exactly is Amazon looking for?

At the end of each interview your interviewer will grade your performance using a standardized feedback form that summarizes the attributes Amazon looks for in a candidate. That form is constantly evolving, but we have listed some of its main components below.

A) Notes

The interviewer will file the notes they took during the interview. This usually includes the questions they asked, a summary of your answers, and any additional impressions they had (e.g. communicated ABC well, weak knowledge of XYZ, etc).

B) Technical competencies

Your interviewer will then grade you on technical competencies. They will be trying to determine whether you are "raising the bar" or not for each competency they have tested. In other words, you'll need to convince them that you are at least as good as the average current Amazon data engineer at the level you're applying for.

The exact technical competencies you'll be evaluated against vary by role. But here are some common ones for data engineering roles:

  • Problem solving
  • Analytical skills
  • Communication and collaboration skills
  • Advanced knowledge of SQL, data modeling and warehousing, ETL development, etc.
C) Leadership principles

Your interviewer will also grade you on Amazon's 16 leadership principles and assess whether you're "raising the bar" for those too. As mentioned above, each interviewer is given two or three leadership principles to grill you on. 

D) Overall recommendation

Finally, each interviewer will file an overall recommendation into the system. The different options are along the lines of: "Strong hire," "Hire," "No hire," "Strong no hire."

1.3 What happens behind the scenes

Your recruiter is leading the process and taking you from one stage to the next. Here's what happens after each of the stages we’ve just described:

  • After the phone screens, your recruiter decides to move you to the onsite or not, depending on how well you've done up to that point.
  • After the onsite, each interviewer files their notes into the internal system, grades you and makes a hiring recommendation.
  • The "Debrief" brings all your interviewers together and is led by the Bar Raiser, who is usually the most experienced interviewer and is also not part of the hiring team. The Bar Raiser will try to guide the group towards a hiring decision. It's rare, but they can also veto hiring even if all other interviewers want to hire you.
  • You get an offer. If everything goes well, the recruiter will then give you an offer, usually within a week of the onsite, but it can sometimes take longer.

It's also important to note that recruiters and people who refer you have little influence on the overall process. They can help you get an interview at the beginning, but that's about it.

2. Example questions

Let’s get into the four primary categories of questions you’ll face in the Amazon data engineer interview.

We’ve analyzed 145 interview questions reported by Amazon data engineer candidates on Glassdoor, and we’ve listed them in order from the most frequently reported to the least frequently reported.

Here are the most common question categories and the frequency at which they appeared in the Glassdoor interview reports:

  • Behavioral interview questions (35%)
  • SQL interview questions (27%)
  • Data management interview questions (24%)
    • Data modeling
    • Data warehousing
    • Data pipelines
  • Coding interview questions (14%)
Amazon data engineer question types

In the sections below, we've put together a high-level overview of each type of question, to help you prepare.

Additionally, we've compiled a selection of real Amazon data engineer interview questions, according to data from Glassdoor. Note that we’ve edited the language in some places to improve the clarity or grammar.

2.1 Behavioral interview questions (35%)

Amazon’s leadership principles tie into every step of the interview process, and interviewers will test your affinity with them through behavioral questions.

Even in the technical rounds, your interviewers are looking for you to live and breathe these 16 principles, so spend extra time studying them.

If you're not already familiar with Amazon's leadership principles, here is the full list:

  1. Customer Obsession - "Leaders start with the customer and work backwards. They work vigorously to earn and keep customer trust. Although leaders pay attention to competitors, they obsess over customers.”
  2. Ownership - "Leaders are owners. They think long term and don’t sacrifice long-term value for short-term results. They act on behalf of the entire company, beyond just their own team. They never say ‘that’s not my job.’”
  3. Invent and Simplify - "Leaders expect and require innovation and invention from their teams and always find ways to simplify. They are externally aware, look for new ideas from everywhere, and are not limited by ‘not invented here.’ Because we do new things, we accept that we may be misunderstood for long periods of time.”
  4. Are Right, A Lot - "Leaders are right a lot. They have strong judgement and good instincts. They seek diverse perspectives and work to disconfirm their beliefs.”
  5. Learn and Be Curious - "Leaders are never done learning and always seek to improve themselves. They are curious about new possibilities and act to explore them.”
  6. Hire and Develop the Best - "Leaders raise the performance bar with every hire and promotion. They recognize exceptional talent, and willingly move them throughout the organization. Leaders develop leaders and take seriously their role in coaching others. We work on behalf of our people to invent mechanisms for development like Career Choice.”
  7. Insist on the Highest Standards - "Leaders have relentlessly high standards — many people may think these standards are unreasonably high. Leaders are continually raising the bar and drive their teams to deliver high quality products, services, and processes. Leaders ensure that defects do not get sent down the line and that problems are fixed so they stay fixed.”
  8. Think Big - "Thinking small is a self-fulfilling prophecy. Leaders create and communicate a bold direction that inspires results. They think differently and look around corners for ways to serve customers.”
  9. Bias for Action - "Speed matters in business. Many decisions and actions are reversible and do not need extensive study. We value calculated risk taking.”
  10. Frugality - "Accomplish more with less. Constraints breed resourcefulness, self-sufficiency, and invention. There are no extra points for growing headcount, budget size, or fixed expense.”
  11. Earn Trust - “Leaders listen attentively, speak candidly, and treat others respectfully. They are vocally self-critical, even when doing so is awkward or embarrassing. Leaders do not believe their or their team’s body odor smells of perfume. They benchmark themselves and their teams against the best.”
  12. Dive Deep - "Leaders operate at all levels, stay connected to the details, audit frequently, and are skeptical when metrics and anecdote differ. No task is beneath them.”
  13. Have Backbone; Disagree and Commit - "Leaders are obligated to respectfully challenge decisions when they disagree, even when doing so is uncomfortable or exhausting. Leaders have conviction and are tenacious. They do not compromise for the sake of social cohesion. Once a decision is determined, they commit wholly.”
  14. Deliver Results - "Leaders focus on the key inputs for their business and deliver them with the right quality and in a timely fashion. Despite setbacks, they rise to the occasion and never settle.”
  15. Strive to be Earth’s Best Employer - “Leaders work every day to create a safer, more productive, higher performing, more diverse, and more just work environment. They lead with empathy, have fun at work, and make it easy for others to have fun. Leaders ask themselves: Are my fellow employees growing? Are they empowered? Are they ready for what's next? Leaders have a vision for and commitment to their employees' personal success, whether that be at Amazon or elsewhere.”
  16. Success and Scale Bring Broad Responsibility - “We started in a garage, but we're not there anymore. We are big, we impact the world, and we are far from perfect. We must be humble and thoughtful about even the secondary effects of our actions. Our local communities, planet, and future generations need us to be better every day. We must begin each day with a determination to make better, do better, and be better for our customers, our employees, our partners, and the world at large. And we must end every day knowing we can do even more tomorrow. Leaders create more than they consume and always leave things better than how they found them.”

To help you start practicing, we've compiled the following list of behavioral questions asked at Amazon data engineer interviews. We recommend that you practice each of them. 

In addition, we also recommend practicing the behavioral questions in our Amazon behavioral interview guide, which covers a broader range of behavioral topics related to Amazon’s leadership principles.

In the questions below, we’ve suggested the leadership principle that each question may be addressing. Let's get to it.

Amazon data engineer interview questions: Behavioral

  • Why Amazon?
  • Tell me about yourself
  • Why do you want to be a data engineer?
  • How did you go above and beyond your role to keep the customers or business owners of your project happy? (Customer obsession)
  • What difference have you made in your current team apart from regular work? (Ownership)
  • Tell me about a time when you broke the status quo. (Invent and simplify)
  • Tell me about a time you applied judgment to a decision when data was not available. (Are right, a lot)
  • What’s the most innovative thing you’ve ever done? (Learn and be curious)
  • What are your future goals? (Learn and be curious)
  • Tell me about a time when a colleague failed at a task, and what did you do? (Insist on the highest standards)
  • What has been your greatest achievement? (Think big)
  • Tell me about a time when you completed a task without informing your manager. (Bias for action)
  • Tell me about the last time you figured out a way to keep an approach simple or to save on expenses. (Frugality)
  • Tell me a time when you had a disagreement with your co-workers. (Earn trust)
  • What would you bring to the team? (Dive deep)
  • Tell me about a time you were in a meeting and had an opinion different from everyone else in the room. What did you do and what were the outcomes? (Have backbone; disagree and commit)
  • What is the most difficult project you’ve ever worked on? (Deliver results)
  • What was your biggest challenge? (Deliver results)
  • Tell me about a time when you made a decision which impacted the team or the company. (Success and scale bring broad responsibility)

2.2 SQL interview questions (27%)

Amazon’s backend handles billions of data fetch operations daily. So engineers must be able to extract and organize huge volumes of structured and unstructured data. Interviewers test this ability with SQL questions.

In these interviews, you will be expected to use SQL to solve real-world problems. You’ll need to know how to use window functions, aggregate functions, subqueries, joins, and sub-selects, as well as handle performance tuning and optimization.

You will also have to apply SQL queries to data modeling and warehousing problems, which we’ll address in the next section.

Let’s take a look at some real examples of SQL questions that we found in the Amazon data engineer Glassdoor interview reports.

Example Amazon data engineer interview questions: SQL

  • Given a dataset, find the time period when the most people were online, measured in seconds.
  • Given a large table with 3 columns (datetime, employee, and customer_response, which is a free text column), with phone number information embedded in the customer_response column, find the top 10 employees with the most phone numbers found in the customer_response column.
  • Given a dataset, display all products with more than 50% increase sales from Previous month to Current Month.
  • Given 1 table with player_id, log in date, and 2 other fields, calculate first day retention rate. (First day retention rate is defined as the player who logs in the 2nd day immediately after the first time they’ve logged in to the game.)
  • The marketing team wants to run a campaign to bring back subscribers who are no longer active. Write a query to pull out subscribers who are no longer active.
  • Write a SQLquery to get total revenue generated by each subscriber in the year 2014.
  • How do you print the usage_amount of previous/consecutive rows b) without using window functions?
  • Based on a specific SQL error, why do you think this error has occurred? How would you investigate? What would you do to fix it?
  • How do you query tune? If a query is taking more time than it initially did, what are the issues that you would look for in order to determine the cause?

2.3 Data management interview questions (24%)

With hundreds of millions of customers and billions of transactions, Amazon collects petabytes of data. Modeling that data, storing it properly, and extracting insights from it is key to a data engineer’s daily work.

Interviewers will ask you to bring datasets together to solve realistic problems Amazon could be facing. You should be able to determine what type of technology is needed to create and manage large datasets, design ETL pipelines, implement big data solutions, and write select SQL statements to produce specific results.

Let’s take a look at some real examples of data management questions that we found in the Glassdoor data.

Amazon data engineer interview questions: Data management

Data modeling

  • How do you create a schema that would keep track of a customer address where the address changes?
  • Design a data model in order to track product from the vendor to the Amazon warehouse to delivery to the customer.
  • Design a data model for a retail store.
  • Create the required tables for an online store: define the necessary relations, identify primary and foreign keys, etc.
  • How do you manage a table with a large number of updates, while maintaining the availability of the table for a large number of users?
  • Should we apply normalization rules on a star schema?
  • What's a chasm trap?

Data warehousing

  • Give a schema for a data warehouse. 
  • Design a data warehouse to capture sales.
  • Design a data warehouse to help a customer support team manage tickets.
  • Can you design a simple OLTP architecture that will convince the Redbus team to give X project to you?

Data pipelines

  • Given scenario A, how would you design the pipeline for ingesting this data?
  • Given a schema, create a script from scratch for an ETL to provide certain data, writing a function for each step of the process.
  • How would you build a data pipeline around an AWS product, which is able to handle increasing data volume?

2.4 Coding interview questions (14%)

Data engineers at Amazon solve some of the company’s biggest challenges with code. Interviewers ask these questions to test your ability to think and solve complex problems in a structured way, through code.

You’ll need to know how to manipulate data structures and use dictionaries, loops and lists, while showing a good understanding of strings, set operations, etc. You may code in your language of choice, although most candidates reported coding in Python.

We recommend reading our guide on how to answer coding interview questions to understand more about the step-by-step approach you should use to solve these questions.

Amazon data engineer interview questions: Coding

  • Write a function to sort an array so it produces only odd numbers.
  • Write a function to find non duplicate numbers in the first list and preserve the order of the list: [1,1,3,2,5,6,5] --> [1,3,2,5,6]
  • Given a list, return the numbers which have maximum count.
  • Given a json object with nested objects, write a function that flattens all the objects to a single key value dictionary.
  • Write code to find the sum of any two numbers in a given array that could be equal to x.
  • Write code to find the maximum number of combinations of infinite coins of {1,2,5} that can add up to make 20 rupees.
  • How do you implement a stack using a linked list?

3. How to prepare

Now that you know what questions to expect, let's dive into how to prepare. Below, we've listed the four steps we recommend taking to prepare as efficiently as possible.

3.1 Learn about Amazon’s culture

Many candidates skip this step. However, it’s important to take the time to learn more about Amazon and whether it’s the right company for you.

Amazon is a prestigious company, so it’s tempting to apply without doing your research first. In our experience, the prestige alone won't make you happy in your day-to-day work. It’s the company culture, people you work with, and type of work that will.

This is also an important step to take in order to prepare for the interviews. Amazon is looking for engineers who will fit in with their culture and are passionate about the company. Coming in with an understanding of the company strategy and your team’s work will show that you’ve done your research.

Here are some resources to help you get started:

3.2 Practice by yourself

Once you’ve learned more about Amazon, study up on the types of questions that you’ll be asked during the interviews.

For behavioral questions, we recommend learning our step-by-step method for answering behavioral questions. You'll want to prepare examples from your past experience that align with each of Amazon’s 16 leadership principles, edit them down, and practice using them to answer the questions in section 2.2.

For SQL questions, you can also find a lot of practice questions on Leetcode or on PostgreSQL. We also recommend reading this article on the 3 types of SQL questions.

For data questions, learndatamodeling.com has some useful videos that explain some of the core concepts of data modeling, and this article goes over helpful best practices when it comes to data warehousing. 

For coding questions, we recommend using our coding interview prep article as your one-stop-shop to guide your prep process. It has a 7-step preparation plan and links to the best resources.

Finally, a great way to practice answering interview questions is to interview yourself out loud. This may sound strange, but it will significantly improve the way you communicate your answers during an interview. Play the role of both the candidate and the interviewer, asking questions and answering them, just like two people would in an interview. Trust us, it really helps!

3.3 Practice with peers

Practicing by yourself is great, but will only take you so far. 

One of the main challenges of technical interviews is that you have to communicate what you are doing as you are doing it. In behavioral interviews, you have to get used to telling your stories in a clear and structured way, then responding to follow-up questions.

That’s why we strongly recommend practicing live interviews with a peer interviewing you.

A great place to start is to practice with friends if you can. You can also sign up to the software engineer waitlist on our free mock interview platform. We'll let you know as soon as we've activated the software engineer category (which includes data engineering) so that you can start practicing with other candidates. 

3.4 Practice with ex-interviewers

The main benefit of practicing with peers is that it's free. But at some point you'll start noticing that the feedback you are getting from peers isn't helping you that much anymore. Once you reach that stage, we recommend practicing with ex-interviewers from top tech companies.

If you know a data engineer who has experience running interviews at Amazon or another big tech company, then that's fantastic. But for most of us, it's tough to find the right connections to make this happen. And it might also be difficult to practice multiple hours with that person unless you know them really well.

Here's the good news. We've already made the connections for you. We’ve created a coaching service where you can practice 1-on-1 with ex-interviewers from leading tech companies. Learn more and start scheduling sessions today.

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