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The Amazon Data Engineer Guide

Overview: Data Engineering at Amazon

Amazon is one of the most influential companies in the world, and it hires thousands of data engineers each year.

Amazon data engineers play an integral role in the company’s data science operations. They’re responsible for wrangling massive datasets, developing scalable engineering solutions, and building data solutions that drive real impact at the company.

That’s one of the key benefits of working as a data engineer at Amazon: you will be driving real impact for the business, for its customers and for its data science teams.

Of course, landing a data engineering job at Amazon is challenging. The interview process is rigorous and technically demanding. Plus, with dozens of departments that hire data engineers, your interview prep strategy often depends on the role and team.

To help, we’ve put together the Amazon Data Engineer Guide. Here you’ll find an overview of data engineering teams at Amazon, what the Amazon interview process is like, and sample Amazon data engineer interview questions.

Amazon Data Engineer Teams


Data engineers at Amazon are responsible for designing, developing and maintaining the core data structures, data models, and data pipelines at Amazon. Amazon data engineers work across a variety of verticals, including:


The Amazon Advertising data engineer team accepts, transforms, and enriches terabytes of data per day. The team leads the development of data systems, tools, and processes to analyze and leverage Amazon Advertising data.

Amazon Alexa

Data engineers on the Alexa team architect, develop and maintain a centralized data system and a single source of truth for the Web Info organization. These teams are responsible for integrating multiple data sources to create BI reports and visualizations.

Amazon Devices

Data engineers on the Devices team are strategic partners to the product managers and engineers on the team. Devices engineers provide expertise on data storage, feature instrumentation, and data privacy, and they create the data infrastructure and pipelines to drive Amazon’s machine learning projects.

Amazon Web Services

The AWS Data Science team uses AWS tools to unify data preparation, machine learning, and model deployment. The engineering team is responsible for scaling the abilities and resources for customers by delivering advanced functionality for data visualization, feature engineering, model interpretability, and low-latency deployment.

Operations Technology

Data engineers on the Operations Technology team tackle some of the most complex challenges in large-scale computing. Most of the work they do involves storing and providing access to data in efficient ways. They deal with very diverse and high-volume data, millions of records per day.


Data Engineers on the Retail team play a significant role in building Amazon’s large-scale, high-volume, high-performance data integration and delivery services. These data solutions are used for periodic reporting and drive business-decision making.

Roles and Responsibilities


Data engineers at Amazon tackle complex, large-scale data engineering challenges, and they work with very diverse and high-volume data. Although the responsibilities vary by vertical, data engineers at Amazon are responsible for:

  • Building different types of data warehousing layers based on specific use cases
  • Building scalable data infrastructure and understanding distributed systems concepts from a data storage and compute perspective
  • Utilizing expertise in SQL and having a strong understanding of ETL and data modeling
  • Ensuring the accuracy and availability of data to customers and understanding how technical decisions can impact their business’s analytics and reporting
  • Proficiency in at least one scripting/programming language to handle large-volume data processing
  • Designing and implementing analytical data infrastructure
  • Interfacing with other technology teams to extract, transform and load data from a wide variety of data sources
  • Collaborating with various tech teams to implement advanced analytics algorithms

The Amazon Data Engineer Interview

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Amazon data engineer interviews are typically broken into three stages: An initial recruiter screen, a technical screen and an onsite round. In the tech and onsite rounds, candidates will be asked questions focusing on core data engineering skills like SQL, data modeling and data warehousing. Here’s a closer look at the process:

Stage 1: Recruiter Screen

The Amazon data engineering interview process starts with a phone screen. A recruiter will call you to assess your technical skills, ask you about your experience, and determine if you’re a right fit for the role.

At this stage, the recruiter wants to understand how proficient you are in programming, typically with Python and SQL. And they want to understand how your experience aligns with Amazon’s culture.

You may be asked basic technical questions like writing simple SQL queries, defining basic Python functions, and in some cases, pandas functionalities.

Tip: Be prepared to talk about your work experience, and map your skills and experiences to Amazon’s core values.

Stage 2: Technical Screen

This interview is typically a 45-minute screen with an Amazon data engineer. This stage of the interview process assesses your technical skills and knowledge. Topics covered in the tech screen include:

  • Data warehousing
  • ETL tools
  • Data structures
  • SQL and Python
  • Data modeling

Commonly, candidates will face a range of SQL questions, covering fundamentals like joins, subqueries and case statements. You may also face a simple data engineering case question.

In this stage, you’ll be assessed on how efficiently you can write code and your comfort with programming languages and tech solutions and concepts used in the job.

Tip: Although speed is assessed, so is your thoroughness. Ask clarifying questions before you jump into a coding solution, if needed. Also, think out loud, to help the interviewer understand your process.

Stage 3: Onsite Interview

The onsite interview is the most rigorous, and you will most likely face a scenario-based case study question, definitions-based technical questions, as well as a “Bar Raiser” interview.

This stage is usually split into three areas:

  • Technical Interviews
  • Bar Raiser Interview
  • HR Interview

In technical interviews, usually you will face 2-4, you’re being assessed on your problem-solving ability. You may face a data engineering case study, and have to use your skills in SQL and Python to solve the problem. You might be required to whiteboard a solution or write code.

Technical interviews may also ask you to investigate a pipeline model, design a data model, or determine what’s causing an ETL error.

amazon data engineer mock interview

Bar Raiser interviews are unique to Amazon. In this stage, an Amazon employee from a different department will ask you questions and determine if you’re the right fit for Amazon.

Tip: These interviews focus on Amazon’s 16 Leadership Principles. Have the ability to map your skills and experience to these 16 principles.

Finally, the HR round focuses on your previous work experiences and projects you’ve worked on in the past. Keep the focus on yourself and data engineering skills in this stage.

Hiring requirements for Amazon data engineering jobs:

The qualifications and required skills vary by department and job role. But in general, most data engineering jobs at Amazon require:

  • Be comfortable with collaboration and have a natural curiosity
  • Degrees in math, computer science, engineering or a related field (or comparable experience in the industry)
  • Proficiency with data modeling, ETL development and data warehousing
  • Comfort with tools like Oracle, Redshift, PostgreSQL
  • Proficiency in programming languages like Python or Java
  • Familiarity with AWS
  • Strong SQL skills, including performance tuning

For more interview prep help, see our guide How to Prepare for Data Engineering Interviews.

Amazon Data Engineer Salary

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