1. What is a Data Engineer at Amazon?

At Amazon, a Data Engineer plays a pivotal role in building the infrastructure that powers one of the world's most data-centric organizations. You are not simply moving data from point A to point B; you are architecting petabyte-scale data lakes, designing robust ETL pipelines, and enabling data-driven decision-making for products ranging from Prime Video and Alexa to the core Retail business and AWS services.

This role requires a blend of software engineering discipline and database expertise. You will work on complex challenges involving high-volume, high-velocity data, ensuring that Data Scientists and Business Intelligence Engineers have the clean, reliable data they need to optimize supply chains, personalize user experiences, and predict future trends. The impact of your work is immediate and tangible—a pipeline optimization you write could save the company millions in compute costs or improve the latency of a customer-facing feature.

Expect to work in an environment that values Ownership and Customer Obsession. You will be tasked with solving ambiguous problems with minimal hand-holding, often building solutions that must scale globally from day one. If you enjoy wrestling with massive datasets and building systems that serve millions of users, this is one of the most exciting environments to do it in.

2. Getting Ready for Your Interviews

Preparing for an Amazon interview requires a shift in mindset. You must demonstrate technical excellence, but equally important is your alignment with the company's core values. Do not underestimate the behavioral component; it carries as much weight as your coding skills.

You will be evaluated on the following key criteria:

Data Engineering Fundamentals – You must demonstrate deep knowledge of database internals, data modeling (dimensional modeling, Star/Snowflake schemas), and ETL design patterns. Interviewers will test your ability to write complex, optimized SQL and your understanding of distributed computing concepts.

Coding & Algorithms – While not as intense as a Software Development Engineer (SDE) interview, you are expected to write clean, production-quality code in Python, Scala, or Java. You will be tested on data structures (arrays, dictionaries, sets) and algorithms suitable for data manipulation.

Amazon Leadership Principles (LPs) – This is the most distinct part of the Amazon culture. You will be evaluated on how you have demonstrated principles like "Dive Deep," "Bias for Action," and "Deliver Results" in your past work. You must prepare stories using the STAR method (Situation, Task, Action, Result) that map directly to these principles.

System Design – For mid-to-senior roles, you will face questions on designing scalable data architectures. You need to know when to use a relational database versus a NoSQL store, how to handle data quality checks, and how to architect for fault tolerance on AWS.

3. Interview Process Overview

The interview process for a Data Engineer at Amazon is rigorous, structured, and designed to eliminate false positives. Based on recent candidate experiences, the process typically moves from an online assessment to a phone screen, culminating in a comprehensive onsite "Loop." The timeline can vary, but Amazon generally moves quickly once you pass the initial screens.

You should expect the process to start with an Online Assessment (OA) focusing on SQL and basic coding, often hosted on platforms like HackerRank. If successful, you will proceed to a technical phone screen (or video call) involving live coding and a deep dive into your resume. The final stage is the "Loop"—a series of 4–5 back-to-back interviews (virtual or in-person) where you will meet with Data Engineers, Managers, and a "Bar Raiser." The Bar Raiser is a designated interviewer from a different team whose sole job is to ensure you are better than 50% of the current employees in the role.

Throughout this process, consistency is key. You might answer a technical question perfectly, but if you fail to demonstrate the Leadership Principles or show a lack of "Customer Obsession," you will likely not receive an offer. The difficulty is generally rated as Medium to Difficult, with a heavy emphasis on your ability to explain why you made specific technical decisions.

This timeline illustrates the standard progression from your application to the final offer. Use the time between the phone screen and the onsite Loop to heavily practice your STAR stories for behavioral questions, as this is where many technically strong candidates fail. Be prepared for a marathon day during the final stage; managing your energy and maintaining enthusiasm through 4+ hours of interviews is essential.

4. Deep Dive into Evaluation Areas

The following areas represent the core pillars of the Amazon Data Engineer interview. You should allocate your study time based on these categories, noting that SQL and Leadership Principles are often the primary filters.

SQL and Data Modeling

This is the bread and butter of the role. You will not just be asked to write queries; you will be asked to optimize them. Interviewers expect you to handle complex aggregations, window functions, and schema designs on the fly.

Be ready to go over:

• Complex SQL Queries – Joins (Inner, Left, Cross), Window Functions (RANK, LEAD, LAG), and CTEs.
• Data Modeling – Designing Star and Snowflake schemas, handling Slowly Changing Dimensions (SCD Type 1, 2, 3).
• Performance Tuning – Understanding execution plans, indexing strategies, and partitioning.
• Advanced concepts – Skew handling in distributed joins and query optimization in columnar databases (like Redshift).

Example questions or scenarios:

• "Write a query to find the top 3 selling products per category for the last month."
• "How would you design a schema to track historical changes in customer addresses?"
• "Debug a query that is running slowly on a billion-row table."

Coding and Algorithms

Amazon expects Data Engineers to be proficient coders. The focus is usually on data manipulation rather than complex graph traversal or dynamic programming, but you must write syntactically correct code.

Be ready to go over:

• Data Structures – Arrays, Hash Maps (Dictionaries), Sets, and Strings.
• Algorithms – Sorting, searching, and two-pointer techniques.
• Scripting – File parsing, string manipulation, and API interaction using Python.

Example questions or scenarios:

• "Given an array of integers, return indices of the two numbers such that they add up to a specific target (Two Sum)."
• "Find the second largest element in an array without sorting."
• "Validate if a string has properly closed parentheses."

Big Data Frameworks & System Design

You need to demonstrate that you can work outside of a single-node database. Questions here focus on the "Engineer" part of the title—building systems that don't crash under load.

Be ready to go over:

• Distributed Processing – Spark (PySpark) fundamentals, RDDs vs. DataFrames, and lazy evaluation.
• AWS Ecosystem – Redshift, S3, EMR, Glue, and Lambda.
• Pipeline Design – Batch vs. Streaming (Kinesis/Kafka), idempotency, and backfilling data.

Example questions or scenarios:

• "Explain how you would migrate an on-premise data warehouse to AWS."
• "How do you handle data skew in a Spark join operation?"
• "Design a pipeline to ingest clickstream data in real-time."

Leadership Principles (Behavioral)

This is not a "soft skill" check; it is a rigorous evaluation. You will be asked "Tell me about a time..." questions for nearly every principle.

Be ready to go over:

• Customer Obsession – Prioritizing customer needs over technical perfection.
• Ownership – Going beyond your job description to fix a problem.
• Bias for Action – Making a calculated decision with incomplete data.
• Have Backbone; Disagree and Commit – Respectfully challenging a decision when you disagree.

5. Key Responsibilities

As a Data Engineer at Amazon, your day-to-day work revolves around enabling data availability and quality. You are the builder who connects raw data sources to analytical endpoints.

You will spend a significant portion of your time designing and maintaining ETL pipelines. This involves writing code to extract data from various internal services, transforming it to meet business logic requirements, and loading it into data lakes (S3) or data warehouses (Redshift). You will frequently collaborate with Software Development Engineers to understand upstream data formats and with Business Intelligence Engineers to understand downstream reporting needs.

Beyond pipeline construction, you are responsible for infrastructure health. You will monitor data quality, troubleshoot pipeline failures, and optimize query performance to ensure SLAs are met. In many teams, you will also be involved in architectural discussions, helping to decide which AWS services to leverage for new initiatives. You are expected to automate manual processes and constantly look for ways to reduce technical debt.

6. Role Requirements & Qualifications

To succeed in this interview and role, you need a specific mix of technical hard skills and adaptive soft skills.

• Technical Skills (Must-Have) – Proficiency in SQL is non-negotiable; you must be able to write advanced queries from scratch. You need strong coding skills in Python or Java. Experience with dimensional data modeling and schema design is essential.
• Technical Skills (Nice-to-Have) – Experience with AWS services (Redshift, Glue, EMR, Athena) is a massive plus. Familiarity with Spark/PySpark and big data frameworks will set you apart. Knowledge of infrastructure as code (Terraform, CloudFormation) is increasingly valuable.
• Experience Level – Typically, candidates have a background in Computer Science or a related field, with prior experience in BI or Data Engineering roles. For L4/L5 roles, interviewers look for evidence of end-to-end project ownership.
• Soft Skills – You must be able to communicate complex technical concepts to non-technical stakeholders. The ability to navigate ambiguity—figuring out what to build when requirements are vague—is critical at Amazon.

7. Common Interview Questions

The following questions are drawn from actual candidate experiences. While you won't see these exact questions every time, they represent the patterns and difficulty level you should expect. Amazon relies heavily on a question bank, so mastering the underlying concepts of these examples is crucial.

Technical: SQL & Coding

These questions test your raw execution ability. Expect to write code on a whiteboard or a shared online editor.

• "Write a query to find the second highest salary in each department."
• "Given a list of integers, find the two numbers that sum up to a specific target."
• "Write a function to validate if a string contains valid parentheses (e.g., (()) is valid, )( is not)."
• "Find the second largest element in an array."
• "Write a complex SQL query involving a self-join to find employees who earn more than their managers."

Technical: Big Data & PySpark

These questions assess your ability to handle scale.

• "How would you optimize a PySpark job that is running slow due to a skewed dataset?"
• "Explain the difference between repartition and coalesce in Spark."
• "How do you handle duplicate data in an append-only log structure?"
• "Describe a time you had to debug a production pipeline failure. What was the root cause?"

Behavioral: Leadership Principles

These are the most critical questions to prepare for. Use the STAR format (Situation, Task, Action, Result) for every answer.

• "Tell me about a time you had to make a decision with incomplete information. (Bias for Action)"
• "Describe a situation where you disagreed with your manager or a team member. How did you handle it? (Have Backbone; Disagree and Commit)"
• "Tell me about a time you went above and beyond your job description. (Ownership)"
• "Give an example of a tough deadline you missed. How did you communicate it? (Deliver Results)"

8. Frequently Asked Questions

Q: How hard are the coding questions compared to SDE roles? The coding questions for Data Engineers are generally Easy to Medium on platforms like LeetCode. You likely won't be asked to invert a binary tree or solve complex dynamic programming problems. However, you must produce clean, compilable code, and you should be very comfortable with array and string manipulations.

Q: What is the "Bar Raiser" interview? The Bar Raiser is a unique Amazon concept. This is an interviewer from a completely different team who has special veto power. Their goal is to ensure you raise the performance bar of the organization. They will often focus heavily on Leadership Principles and probe deep into your behavioral examples to check for consistency.

Q: Can I use Python for all coding rounds? Yes, Python is the industry standard for Data Engineering and is widely accepted at Amazon. You can also use Java or Scala if you prefer, but Python is often recommended for its brevity in whiteboard interviews.

Q: How much does domain knowledge matter (e.g., AWS tools)? While knowing AWS tools (Redshift, EMR, Glue) is a significant advantage, Amazon hires for fundamental engineering smarts. If you are an expert in Spark and SQL but have only used Azure or GCP, you can still get hired. They assume you can learn the specific tools on the job.

Q: Is the interview process remote? Currently, most "loops" are conducted virtually via Amazon Chime. However, the format mimics the onsite experience: back-to-back video calls spanning several hours.

9. Other General Tips

Master the STAR Format: When answering behavioral questions, be structured. deeply. Spend 10% on the Situation, 10% on the Task, 60% on the Action (what you specifically did, not "we"), and 20% on the Result (use numbers and metrics).

"I" vs. "We": In your stories, avoid saying "we did this." Interviewers want to know what you contributed. If you say "we," they will interrupt you to ask, "But what was your specific role?"

Clarify Before You Code: Never jump straight into writing code. Ask clarifying questions about edge cases, data volume, and input formats. This shows you have a "Dive Deep" mentality and prevents you from solving the wrong problem.

Know Your SQL Joins Cold: You will likely be asked to write a query involving multiple joins. Be careful with LEFT JOIN vs INNER JOIN logic, as this is a common trap interviewers set to test your attention to detail.

10. Summary & Next Steps

Becoming a Data Engineer at Amazon is a career-defining opportunity. You will work on systems that operate at a scale few other companies can match, and you will develop a rigorous engineering discipline that is respected across the industry. The role demands technical precision, but it rewards those who can think big and take ownership of their work.

To succeed, focus your preparation on three pillars: Advanced SQL, Python scripting, and Leadership Principles. Do not let the behavioral portion be an afterthought; for many candidates, it is the deciding factor. Practice your STAR stories until they feel natural, and ensure you can articulate the business impact of your technical choices.

This salary data provides a baseline for what you can expect. Compensation at Amazon is heavily weighted towards Restricted Stock Units (RSUs), which vest over four years (often back-weighted). When evaluating an offer, look at the "Total Compensation" (Base + Sign-on Bonus + RSUs) rather than just the base salary.

You have the roadmap. Now, it is time to execute. Review the questions, sharpen your SQL skills, and prepare to show Amazon why you are a builder. Good luck!