Amazon Data Engineer Interview Guide

Amazon’s data engineer interviews are fast, intense, and heavily shaped by the company’s Leadership Principles. The loop tests how you design and optimize large-scale data systems, write clean SQL and Python, and navigate tough behavioral questions under pressure.

Across every stage—technical screens, data modeling, and the final onsite—Amazon interviewers evaluate your engineering fundamentals, decision-making, and how you think about customer impact. Behavioral questions appear in nearly every round.

This guide breaks down the full interview process, what Amazon 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 varies by team, but the core structure is consistent. Most candidates move from recruiter screen to final decision in about 3–6 weeks. Expect technical depth, team-specific questions, and Leadership Principle assessments throughout the loop.

Interviews happen in 4 stages:

1. Recruiter screen or async resume screen: Background, motivation for Amazon, compensation expectations, and basic fit
2. Technical phone screen: SQL, coding, or data modeling problems
3. Second technical screen (if needed): Additional signal on SQL, ETL, or AWS data services
4. Final onsite loop: 4–6 rounds covering domain-specific technical skills, SQL, system design, ETL, and Leadership Principles

Teams personalize the technical rounds. For example, a Prime Video team may ask about optimizing data ingestion for large-scale streaming workloads, while an AWS Redshift team may focus on data warehousing and performance tuning.

Recruiter screen

The recruiter screen is a short call or an async resume review to confirm basic fit. Amazon recruiters move fast and tend to be more direct than at most companies, especially when discussing compensation and level.

They want to see whether your background lines up with the job description, whether you can communicate clearly, and whether you understand why you’re applying to Amazon.

Interviewers look for a few core signals:

• Clear explanation of your current role and responsibilities
• Ability to summarize your impact with metrics
• Genuine motivation for Amazon, grounded in customer-focused work
• Alignment with relevant Leadership Principles
• Confidence and steadiness when discussing compensation

Sample questions

• 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?

Technical screen

The technical screen is usually a 45–60 minute interview focused on SQL, data modeling, and Python coding. Amazon tailors this round to each team, so the exact mix of questions depends on the service you’re interviewing for. It’s common for interviewers to slip in 1–2 Leadership Principle questions as well.

This round evaluates how you structure data problems, write efficient queries, and think about performance at scale. If the interviewer needs more signal, Amazon may schedule a second technical screen.

Interviewers typically look for:

• Strong SQL fundamentals and query optimization
• Practical understanding of ETL pipelines and data modeling
• Clean, readable Python for data-heavy tasks
• Ability to reason about large-scale or distributed datasets
• Clear explanations and steady communication under pressure

Sample questions

• 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.

Final onsite loop

The final onsite loop includes 4–6 rounds covering SQL, data modeling, system design, coding, and Leadership Principles. Amazon interviewers evaluate both your technical depth and how you make decisions, handle ambiguity, and deliver results at scale.

Interviewers look for:

• Deep domain knowledge relevant to the team (warehousing, Redshift, streaming, S3, ETL)
• SQL fluency and ability to optimize large queries
• Clear system design reasoning for high-volume data workflows
• Strong ownership and customer-focused decision-making
• Concise, structured communication under pressure

SQL, data management, and ETL interviews

Amazon leans heavily on SQL and data management questions. These rounds test how you work with large datasets, optimize queries, and design reliable ETL pipelines. Expect detailed follow-ups on performance, indexing, partitioning, and AWS data services.

For many candidates, this is one of the most important parts of the loop—Amazon interviewers want proof that you can reason about real production workloads at scale.

Interviewers typically look for:

• Strong SQL fundamentals and query performance tuning
• Experience with data modeling and warehouse design
• Understanding of AWS-native tools (Redshift, S3, Glue, EMR, DynamoDB)
• Ability to design scalable data pipelines
• Familiarity with PySpark or distributed data processing frameworks

These conversations often go deep. Interviewers want to see that you understand why a design choice works—not just that you’ve seen it before.

Sample questions

• Design an ETL process to collect tickets in real time.
• How many steps or stages would you include in a robust pipeline?
• How would you optimize a slow Redshift query scanning billions of rows?
• Model a scalable schema for storing clickstream events.
• Walk me through how you’d partition large datasets in S3 for efficient queries.

System design interviews

Amazon’s system design rounds focus on practical, data-heavy architecture problems. Instead of abstract “design X” prompts, you’ll often be asked to design a real component tied to the team’s domain—for example, 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 prioritize performance and reliability.

Interviewers look for:

• Clear understanding of core use cases and data flows
• Ability to choose storage, compute, and messaging components that fit scale
• Strong reasoning around performance, bottlenecks, and failure modes
• Familiarity with AWS-native architectures and trade-offs
• Structured communication as you design in real time

Common topics

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

Domain-specific interview

Some Amazon teams include a domain-specific round tailored to the service you’d join. This is where interviewers test your familiarity with the team’s actual data problems, tools, and workflows.

A Redshift team may ask about warehouse optimization, while a Prime Video team may dig into streaming ingestion, encoding metadata, or S3 access patterns.

Interviewers look for:

• Awareness of the team’s product, service, or data architecture
• Ability to reason about real constraints the team handles day-to-day
• Familiarity with AWS-native systems connected to the domain
• Practical decision-making grounded in performance and scalability

Coding interviews

The coding rounds test how you solve medium-difficulty algorithmic problems using clear reasoning and clean Python. Amazon’s data engineering interviews favor graphs, strings, and HashMap–based logic, with far fewer dynamic programming questions than typical SWE interviews.

Interviewers look for:

• Ability to break problems down and identify constraints quickly
• Clean, readable Python or SQL-adjacent logic
• Familiarity with common patterns: BFS/DFS, sets, maps, sliding windows
• Practicality over cleverness—Amazon values correctness and clarity
• Willingness to test your own code out loud

If you want structured practice, try peer mocks or warm-up problems in Exponent’s data engineering course. These mirror the difficulty and pacing of Amazon’s screens.

Sample questions

• Given a string, write a function “recurring_char” 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) interviews

Behavioral evaluations might be the most important part of Amazon’s hiring process. Amazon uses these questions to judge how you make decisions, handle pressure, earn trust, and take ownership.

Above all, interviewers want to see Customer Obsession. It’s the single most valuable signal you can broadcast, even when the question seems like it’s about something else.

Interviewers look for:

• Clear storytelling with measurable impact
• Customer-first reasoning woven into your decisions
• Ownership and bias for action during ambiguous situations
• How you earn trust, learn from failure, and handle conflict
• Concise communication with strong follow-up answers

Trying to guess which Leadership Principle (LP) a question maps to usually backfires. Many prompts are intentionally disguised.

For example, “Tell me about a conflict with your manager” may sound like Have Backbone; Disagree and Commit, but is often scored for Earn Trust.

“Why Amazon?”

This question is a direct LP test. Interviewers want to hear examples that demonstrate how you think about customers, iteration, and impact—not general praise for Amazon.

Sample questions

• Tell me about a time you made a short-term sacrifice for a long-term gain.
• Tell me about a time you failed. What did you learn?
• Tell me about a time you earned someone’s trust.
• Describe a time you disagreed with a decision and what you did next.
• Tell me about your most challenging data problem and how you navigated it.

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, that means improving data warehouses, designing ETL pipelines, tuning performance, and delivering production-ready datasets to stakeholders.

As you grow, the scale of the data grows with you. Early-career engineers may work on a few terabytes of Redshift cleanup each day, while senior engineers handle pipelines moving hundreds of terabytes daily across Amazon’s ecosystem.

Core responsibilities

• 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
• Supporting stakeholders with production-quality datasets

How to prepare before you apply

• Apply to multiple teams: Amazon allows parallel loops, which increases your chances of landing a match. Some candidates interview with up to 9 teams at once.
• Define your niche: Map what you’re best at—cloud pipelines, analytics engineering, NoSQL modeling—to a relevant Amazon team
• Revamp your resume: Include at least 5 impact metrics. Amazon screens hard for quantifiable outcomes. Use Exponent’s resume tools.
• Practice with mocks: Use Exponent’s environments for SQL, coding, and Leadership Principles prep:
  • Data engineering course
  • Leadership Principles course
  • Peer practice
• Optimize your LinkedIn: Amazon recruiters are active and fast. A clean, keyword-rich profile increases passive inbound messages. Securing an Amazon referral also boosts visibility.

Additional resources

• A primer for Amazon’s Leadership Principles interviews
• Exponent’s data engineering interview prep course
• Exponent’s coding course for SWE fundamentals

FAQs about the Amazon Data Engineer interview

What can I expect from my Amazon Data Engineer interview?

You can expect an intense, fast-moving loop with highly specific behavioral expectations. Amazon interviewers press hard on Leadership Principles—especially Customer Obsession—and will weave LP questions into SQL, system design, and coding rounds. Technically, you’ll be tested on SQL, ETL, data modeling, AWS data services, and the domain of the team you’re interviewing with.

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, depending on scheduling and whether you interview with multiple teams in parallel.

How much do Amazon Data Engineers make?

Amazon Data Engineers earn between ~$143K and ~$255K total compensation, depending on level.

• L4 (junior) roles average ~$143K
• L5 (mid-level) roles average ~$192K
• L6 (senior) roles average ~$255K

Compensation is unique due to upfront bonuses (paid over 2 years) and backloaded equity.

What are the typical job requirements for Amazon Data Engineers?

Most roles list 3+ years of experience, but job descriptions are wish lists—not strict requirements. Amazon looks for strengths in:

• Data modeling, warehousing, and ETL
• SQL and Python fundamentals
• AWS (Redshift, S3, Glue, DynamoDB)
• Experience with NoSQL or distributed data systems

Strong candidates can compensate for missing areas if they show ownership and technical depth.

How should I prepare for an Amazon Data Engineer interview?

Prepare by practicing both technical depth and Leadership Principles. Unearth details and metrics from your work history, focus on customer-driven decisions, and build a story bank for LP questions. Then sharpen SQL, warehousing, and domain-specific skills with targeted practice in Exponent’s data engineering course. Studying the team’s service history—especially AWS blog posts—gives you a major edge.