Amazon Data Engineer Interview

Amazon's data engineering interview is team-dependent from start to finish; two candidates interviewing at the same level can face entirely different technical questions depending on the org. Behavioral and technical preparation carry equal weight across every round, which makes this one of the harder FAANG+ loops to prepare for.

This guide breaks down each stage of the Amazon data engineer interview process, what interviewers look for, and how to prepare with example questions, actionable tips, and resources.

Amazon data engineer interview process

Amazon's data engineer interview process threads Leadership Principle questions into every stage, from the recruiter screen through the final onsite loop. The structure below reflects a common path, but round count, technical focus, and entry point vary by team.

Here's what the interview process can look like:

• Recruiter screen: Background, motivation for Amazon, compensation expectations, and basic fit
• Online assessment (some teams): SQL and coding challenges on HackerRank, typically five questions covering joins, aggregations, and window functions
• Technical phone screen: 1-2 interviews with SQL, data modeling, Python/ETL, and Leadership Principle questions
• Final onsite loop: 3-5 rounds covering SQL, system design, ETL/data modeling, coding, and Leadership Principles, including a Bar Raiser round with an interviewer from outside the hiring team

Most candidates move from recruiter screen to final decision in 3-6 weeks. Some teams start with the online assessment before the technical phone screen; others skip it entirely.

Recruiter screen

Amazon data engineer recruiter screens run 30 minutes and serve as a fast alignment check on background, motivation, and level. Amazon recruiters tend to be more direct than at other companies, especially when discussing compensation and leveling.

Expect straightforward questions about your current role, why Amazon, and what you're targeting in your next position. Keep your introduction to 30-45 seconds so you have time to connect your experience to the team's requirements. Securing an Amazon referral can also increase your visibility with recruiters before the screen.

Interviewers look for:

• Role alignment: Whether your background and technical experience match the job description
• Impact clarity: Your ability to summarize past work with specific metrics
• Customer-focused motivation: Genuine interest in Amazon grounded in customer-driven work, not generic praise
• Leadership Principle signal: Early alignment with relevant Leadership Principles
• Compensation readiness: Confidence and clarity when discussing expectations

Sample questions

Here are some real interview questions reported by candidates:

• Walk me through your background.
• Why Amazon?
• What are you looking for in your next role?
• How does your experience align with this team's requirements?
• What are your compensation expectations?

Online assessment

Some Amazon teams begin with an online assessment on HackerRank before scheduling the technical phone screen. The assessment typically includes five SQL and coding challenges and runs 90-120 minutes.

Data engineering questions focus on SQL fundamentals (joins, aggregations, window functions, subqueries) and may include basic Python data manipulation tasks. Accuracy matters, but so does efficiency; interviewers review both correctness and query design.

Interviewers look for:

• SQL fluency: Correct use of joins, CTEs, window functions, and aggregations under time pressure
• Query efficiency: Optimized approaches, not just correct output
• Edge case handling: Attention to nulls, duplicates, and boundary conditions
• Clean code: Readable, well-structured solutions

Sample questions

Here are some real interview questions reported by candidates:

• Write a query to calculate total sales per user from an orders table.
• Identify the top 3 products by month-over-month revenue growth.
• Find all users who placed orders in the last 30 days.
• Remove duplicate records from a dataset while preserving the most recent entry.

Technical phone screen

The Amazon data engineer technical phone screen runs 60-75 minutes and tests how you structure data challenges, write efficient queries, and reason about performance at scale. Each screen typically includes one or two Leadership Principle questions alongside the technical work. If the interviewer needs more signal, Amazon may schedule a second screen.

Interviewers look for:

• SQL depth: Strong fundamentals plus query optimization for large datasets
• ETL reasoning: Practical understanding of pipeline design, data extraction, and transformation logic
• Python competence: Clean, readable code for data-heavy tasks
• Scale awareness: Ability to reason about distributed datasets and performance constraints
• Communication under pressure: Clear explanations of your approach as you work

Sample questions

Here are some real interview questions reported by candidates:

• Write a SQL query to identify users with consecutive daily activity.
• Model a schema for tracking inventory changes across multiple warehouses.
• Given two large datasets in S3, design an efficient process to join and aggregate them.
• Explain how you'd optimize a slow Redshift query scanning billions of rows.

SQL, data management, and ETL rounds

Amazon's data engineer SQL and ETL rounds test how you work with large datasets, optimize queries, and design reliable pipelines. These are often the most heavily weighted technical rounds in the loop, with follow-ups on performance, indexing, partitioning, and AWS data services.

Interviewers look for:

• Window function mastery: Fluent use of ROW_NUMBER, RANK, LAG/LEAD, and rolling aggregations
• Query optimization: Indexing strategies, partition pruning, and performance tuning for large-scale queries
• Data modeling depth: Experience designing fact/dimension schemas, handling slowly changing dimensions, and choosing partition and sort key strategies
• Pipeline reliability: Approaches to idempotency, late-data reprocessing, and streaming vs. batch trade-offs
• AWS tooling fluency: Practical knowledge of Redshift, S3, Glue, EMR, and DynamoDB

Sample questions

Here are some real interview questions reported by candidates:

• Design an ETL process to collect event data in real time.
• How would you optimize a slow Redshift query scanning billions of rows?
• Model a scalable schema for storing clickstream events, including partition and sort key decisions.
• Walk through how you'd handle late-arriving data in a batch pipeline.
• Design a fact/dimension schema for an ecommerce transactions table, including SCD handling.

System design rounds

Amazon data engineer system design rounds focus on practical, data-heavy architecture tied to the team's domain. Instead of abstract prompts, expect to design a real component: an inventory tracking module, a streaming ingestion system, or a high-volume analytics workflow.

These rounds test how you structure large-scale data systems, reason about constraints, and make design decisions that balance performance, reliability, and cost.

Interviewers look for:

• Use case clarity: Ability to identify core data flows and access patterns before choosing components
• Component selection: Sound reasoning about storage, compute, and messaging tools that fit the scale
• Bottleneck awareness: Strong thinking around latency, throughput, failure modes, and scaling patterns
• AWS architecture fluency: Familiarity with Redshift, EMR, Glue, S3, Kinesis, and their trade-offs
• Real-time communication: Structured explanations of your design decisions as you build

Common system design topics

Here are some real topics reported by candidates:

• Distributed systems fundamentals
• Event-driven architecture and streaming ingestion
• Data warehouse and lakehouse design
• AWS big data services (Redshift, EMR, Glue, S3, Kinesis)
• Designing for scale, availability, and cost efficiency

Coding rounds

Amazon data engineer coding rounds test medium-difficulty coding challenges with an emphasis on clean reasoning and readable Python. Amazon's data engineer interviews favor graphs, strings, and HashMap-based logic, with fewer dynamic programming challenges than typical SWE interviews.

Interviewers value correctness and clarity over cleverness. Walk through your approach before writing code, test edge cases out loud, and keep your solutions readable.

Interviewers look for:

• Constraint identification: Ability to break challenges down and spot constraints quickly
• Clean implementation: Readable Python or SQL-adjacent logic with clear variable names and structure
• Pattern recognition: Familiarity with BFS/DFS, sets, maps, sliding windows, and deduplication patterns
• Practical judgment: Preference for correct, maintainable solutions over overly clever ones
• Self-testing discipline: Willingness to walk through your own code and catch edge cases

Sample questions

Here are some real interview questions reported by candidates:

• Given a string, write a function that returns the first recurring character, or None if there isn't one.
• Traverse a graph and detect whether a cycle exists.
• Remove duplicates from a list while preserving order.
• Merge overlapping intervals.

Leadership Principles (behavioral) rounds

Amazon data engineer behavioral rounds may be the most decisive part of the loop. Amazon uses Leadership Principle (LP) questions to evaluate how you make decisions, handle pressure, earn trust, and take ownership.

Customer Obsession is the single most valuable signal you can demonstrate. Even when a question seems to be testing a different principle, grounding your answer in customer impact strengthens it.

Interviewers look for:

• Structured storytelling: Clear, structured answers with measurable impact
• Customer-first reasoning: Decisions framed around user needs and business outcomes
• Ownership and a bias for action: Examples of driving results through ambiguity
• Trust and conflict resolution: How you earn trust, navigate disagreement, and learn from failure
• Depth under follow-up: Ability to go 3-5 layers deep on every story with metrics, trade-offs, and lessons

"Why Amazon?"

The "Why Amazon" question is a direct Leadership Principle assessment. Interviewers are listening for specific examples that show how you think about customers, iteration, and impact.

In your answer, connect a personal experience to Amazon's customer-focused operating model. An example of building something based on user feedback, or iterating on a data product after direct customer input, demonstrates Customer Obsession without naming the principle.

Sample questions

Here are some real interview questions reported by candidates:

• Tell me about your most challenging data challenge and how you navigated it.

Bar Raiser round

Amazon's Bar Raiser is an interviewer from outside the hiring team with veto authority over the final hiring decision. You won't know which interviewer is the Bar Raiser; Amazon intentionally keeps this hidden so candidates perform consistently across every round. After the loop, the Bar Raiser leads the debrief where all interviewers share their assessments and reach a hiring decision.

The Bar Raiser round covers both technical judgment and Leadership Principles, and candidates consistently describe it as the hardest conversation in the loop. Bar Raisers tend to ask broader, principle-driven questions rather than role-specific ones, and Dive Deep and Deliver Results are the Principles most likely to trigger a veto.

Bring your most detailed stories and be ready to explain trade-offs, what you'd change in hindsight, and how your decisions connected to business outcomes.

How to prepare for the Amazon data engineer interview

1. Build a Leadership Principles story bank: Map 6-8 structured stories to the most common data engineering LPs (Customer Obsession, Dive Deep, Deliver Results, Insist on Highest Standards, Are Right A Lot) before you begin your technical prep.
2. Sharpen SQL with Amazon-specific patterns: Practice window functions (ROW_NUMBER, RANK, LAG/LEAD), CTEs, Top-N queries, rolling aggregations, and YoY/MoM growth calculations. Amazon interviewers push past correct answers into optimization and edge cases.
3. Study the hiring team's service area: Amazon's interview is team-dependent, and domain-specific questions tied to the team's data stack are common. A Prime Video team may focus on streaming ingestion, while an AWS Redshift team tests warehouse optimization. Read the AWS database blog for posts from your target team or adjacent services to calibrate your examples.
4. Practice ETL and data modeling under pressure: Focus on idempotency, late-data reprocessing, SCD handling, and streaming vs. batch trade-offs. These topics surface repeatedly in Amazon data engineering loops.
5. Run timed mock interviews: Simulate the full loop under realistic conditions with peer mock interviews and expert coaching. Combine SQL, system design, and behavioral rounds in a single session to practice switching between question types under time pressure.
6. Tailor your resume for Amazon's screening bar: Include at least five quantified impact metrics. Amazon recruiters screen closely for measurable outcomes, and a generic resume is one of the most common reasons candidates don't advance to the technical phone screen.

About the Amazon data engineer role

Amazon data engineers build and maintain the large-scale data infrastructure that powers business decisions across the company.

Day-to-day responsibilities include:

• Building and optimizing ETL pipelines
• Data modeling and warehouse design (often Redshift or NoSQL)
• Performance tuning for massive datasets
• Designing scalable systems for analytics and reporting
• Delivering production-ready datasets to stakeholders

Amazon data engineer experience requirements

Most Amazon data engineer job descriptions list 3+ years of experience. Core strengths Amazon looks for include data modeling, warehousing, and ETL; SQL and Python fundamentals; AWS services (Redshift, S3, Glue, DynamoDB); and experience with NoSQL or distributed data systems.

Strong candidates can compensate for gaps in specific tools if they demonstrate ownership, technical depth, and the ability to learn quickly.

Additional resources

• Data engineering interview prep course
• Data engineering interview overview
• Top data engineering interview questions
• Software engineering interview prep course
• Top SQL data engineering interview questions
• Amazon Leadership Principles interview course
• Amazon Leadership Principles interview guide
• Real Amazon interview experiences

FAQs about the Amazon data engineer interview

What is the Amazon data engineer interview like?

Amazon's data engineering interview is fast-paced and technically demanding, with Leadership Principle questions woven into every round alongside SQL, ETL design, data modeling, system design, and coding. The technical focus varies by hiring team, so candidates interviewing for different orgs face different questions. A Bar Raiser, an interviewer from outside the hiring team with veto authority, evaluates every candidate in the final loop.

How long is the Amazon data engineer interview process?

The Amazon data engineer interview process typically takes 3-6 weeks from recruiter screen to final decision. Timelines vary depending on scheduling, whether you're interviewing with multiple teams in parallel, and whether your loop includes an online assessment before the technical phone screen.

How much does an Amazon data engineer make?

Here are the reported compensation ranges by level for Amazon data engineers, according to Levels.fyi:

• L4 (Data Engineer): ~$143K
• L5 (Data Engineer): ~$199K
• L6 (Senior Data Engineer): ~$258K

Amazon's compensation includes base salary, RSUs on a backloaded 4-year vesting schedule (5/15/40/40), and sign-on bonuses paid over the first two years.

How should I prepare for an Amazon data engineer interview?

Prepare for the Amazon DE interview by splitting your time equally between Leadership Principles and technical prep, since LP questions appear in every round of the loop. Build structured stories for the Principles most common in data engineering loops (Customer Obsession, Dive Deep, Deliver Results), then focus SQL practice on window functions, CTEs, and query optimization. Study the hiring team's service area and run timed mock interviews that combine technical and behavioral rounds in a single session.