Amazon Data Engineer Interview Guide

Amazon's data engineer interview evaluates Leadership Principles as closely as SQL and data modeling, with behavioral questions in every round, recruiter call included. Data modeling is the real filter on the technical side: you'll design a schema from scratch, often for an Amazon business, and defend your grain, keys, and partitioning through extended follow-up questions. The hiring team sets the rest of the technical mix, so start with one defensible warehouse design and a story bank that covers several Principles.

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

The Amazon data engineer interview process starts with a recruiter screen, followed by 1 to 2 technical phone screens and an onsite loop that includes a Bar Raiser. Some teams add an online assessment and hold the phone screen before the recruiter call, and the hiring team decides which technical areas each round covers.

Here's what the interview process can look like:

• Recruiter screen: Short call (often about 30 minutes) covering background, motivation for Amazon, level fit, and at least one Leadership Principle question
• Online assessment (some teams): Online SQL and coding assessment, plus work-style questions in some versions
• Technical phone screen: 1 to 2 interviews of 60-75 minutes each covering SQL, data modeling, Python or ETL, and one Leadership Principle question
• Onsite loop: 3-5 interviews of 45-60 minutes each covering SQL, data modeling, ETL design, coding, system design, and Leadership Principles, including a Bar Raiser

Expect 3-6 weeks from recruiter screen to final decision, with longer gaps between rounds on some teams.

Amazon aims to share a decision within 2 business days of a phone screen and 5 business days of the onsite loop.

Here's where each focus area appears in the loop:

Recruiter screen

The Amazon data engineer recruiter screen is a short call, often about 30 minutes, that checks your background, motivation for Amazon, level, and compensation expectations. Expect Leadership Principle questions and a walkthrough of past projects on this call, and on some teams a light SQL or Python check.

An Amazon referral can increase your visibility with recruiters before the screen.

> Be ready to explain the architecture, metrics, and business impact of your past projects in detail.
– Recent Amazon data engineer candidate

Interviewers look for:

• Role alignment: Whether your background and technical experience match the job description
• Project depth: Your ability to explain the architecture, metrics, and business impact of past data projects
• Customer-focused motivation: A reason for joining Amazon grounded in customer-driven work
• Leadership Principle signal: Early evidence of the Leadership Principles in how you describe past decisions and mistakes
• Compensation readiness: Clear expectations for level and compensation

Recently asked questions

Here are some real interview questions reported by candidates:

• Walk me through your background.
• Why Amazon?
• Tell me about your past projects.
• Tell me about a time you made a mistake.
• What are you looking for in your next role?
• What are your compensation expectations?

Online assessment

Some Amazon data engineer teams start with an online assessment before the technical phone screen. The SQL portion focuses on SQL fundamentals such as joins, aggregations, window functions, and subqueries.

Expect a Python or coding challenge alongside the SQL work. Some versions may also include multiple-choice questions that ask you to identify incorrect queries and work-style scenarios tied to the Leadership Principles.

Entry-level and intern assessments may also include standard data structures challenges.

Interviewers look for:

• SQL fluency: Correct use of joins, CTEs, window functions, and aggregations under time pressure
• Query efficiency: Approaches that scale to large tables once the output is correct
• Edge case handling: Attention to nulls, duplicates, and boundary conditions
• Query reading: Your ability to spot errors in queries you didn't write
• Clean code: Readable, well-structured Python and SQL

Recently asked 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.
• Given an array of integers, return the two values that sum to a target.

Technical phone screen

The Amazon data engineer technical phone screen is a 60-75 minute interview covering SQL, data modeling, and Python or ETL work, with one Leadership Principle question in each screen. Some teams schedule a second technical screen before the onsite loop.

Interviewers ask follow-up questions about your design choices after the first answer. Explain each join, filter, and schema decision as you write it.

Interviewers look for:

• SQL depth: Window functions, ranking, and aggregations written correctly on the first pass
• Design reasoning: Clear justification for schema and query choices when the interviewer questions them further
• ETL reasoning: Practical understanding of extraction, transformation logic, and pipeline reliability
• Python competence: Clean, readable code for data manipulation tasks
• Scale awareness: How your approach changes for distributed datasets and large tables

Recently asked questions

Here are some real interview questions reported by candidates:

• Calculate the average salary for each department.
• Find the top 3 unique salaries in each department and every employee who earns one of them.
• 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 and ETL rounds

Amazon's data engineer SQL and ETL rounds test how you query large datasets and design reliable pipelines with AWS data services. The ETL round focuses on which AWS services you'd combine for a pipeline and how that pipeline handles late, duplicate, or failed data.

Interviewers follow up on performance, indexing, and partitioning once your query returns correct results. SQL prompts commonly use retail, order, and employee tables, so practice on similar schemas.

Interviewers look for:

• Window function fluency: Correct use of ROW_NUMBER, RANK, LAG/LEAD, and rolling aggregations
• Query optimization: Indexing strategies, partition pruning, and performance tuning for large tables
• Pipeline reliability: Approaches to idempotency, retries, and late-data reprocessing
• Processing trade-offs: Sound reasoning about batch vs. streaming for the use case
• AWS service selection: Practical choices among Redshift, S3, Glue, EMR, Kinesis, and DynamoDB

Recently asked questions

Here are some real interview questions reported by candidates:

• Design an ETL process to collect event data in real time.
• Design a pipeline that updates hourly and powers a dashboard of the most common Alexa user requests by country.
• Walk through how you'd handle late-arriving data in a batch pipeline.
• Write a query to find periods of 3 or more consecutive days when stadium attendance was 100 or higher.
• Find the product category with the second-most customers shopping in a given month.

Data modeling round

The Amazon data engineer data modeling round asks you to design a schema from scratch for a business scenario, then defend it through extended follow-up questions. Expect prompts built on Amazon's own businesses, such as Prime memberships, retail orders, and customer data.

Some prompts state production data volumes, such as the daily row count of a fact table and the total size of a dimension table. When a prompt doesn't, ask about data volume and growth before you choose keys and partitions.

Senior candidates may also walk through layered designs such as a medallion architecture. Expect to define each layer's purpose, primary key, and partition key, and how you'd enable fast lookups.

Interviewers look for:

• Requirements first: How you confirm the business questions and query patterns before drawing tables
• Fact and dimension design: A clear grain for each fact table and well-scoped dimensions
• Change handling: Sound treatment of slowly changing dimensions, such as a customer's Prime status
• Keys and partitioning: Primary key, partition key, and sort key choices that support fast lookups at scale
• Volume awareness: Aggregation and storage choices that fit the data volumes stated in the prompt

Recently asked questions

Here are real, recent interview questions reported by candidates:

• Design a data model for Amazon Prime.
• Build a medallion data model for retail data. Walk through each layer, how you'd define the primary key and partition key, and how you'd enable fast lookups.
• FCT_ORDER_ITEM adds 100M rows a day across 5 years, and DIM_CUSTOMER holds 1B rows. Design the model behind weekly, monthly, and yearly geographic and demographic dashboards.
• Design a fact/dimension schema for an ecommerce transactions table, including how you'd handle Prime status as a slowly changing dimension.
• Model a scalable schema for storing clickstream events, including partition and sort key decisions.
• Design a data model for a lending product.

System design rounds

Amazon data engineer system design rounds focus on data architecture tied to the team's domain, such as an inventory tracking module, a streaming ingestion system, or a high-volume analytics workflow. On some teams, the ETL architecture round covers this work; others run a separate design round.

Separate design rounds may add distributed systems fundamentals, including how you'd shard data, when to use OLTP vs. OLAP systems, and when row or columnar storage fits.

Interviewers look for:

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

Common system design topics

Here are some real topics reported by candidates:

• Schema selection and justification for a given workload
• Batch pipeline design and slowly changing dimension choices
• Distributed systems fundamentals, including data sharding and consistent hashing
• Event-driven architecture and streaming ingestion
• Data warehouse and lakehouse design
• OLTP vs. OLAP systems and row vs. columnar storage
• AWS big data services (Redshift, EMR, Glue, S3, Kinesis)
• Designing for scale, availability, and cost efficiency

Coding rounds

Amazon data engineer coding rounds test easy-to-medium Python challenges, with an emphasis on clean reasoning and readable code. Expect standard data structures challenges alongside Python data manipulation tasks, with entry-level loops focusing more on data structures. Some loops ask you to write code on paper or without an IDE.

Walk through your approach before you write code, then test edge cases out loud.

Interviewers look for:

• Constraint identification: Your ability to break a challenge down and spot constraints quickly
• Clean implementation: Readable Python with clear variable names and structure
• Pattern recognition: Familiarity with BFS/DFS, sets, maps, intervals, and deduplication patterns
• Maintainable solutions: Correct, readable code a teammate could extend
• Self-testing discipline: A walkthrough of your own code that catches edge cases

Recently asked questions

Here are some real interview questions reported by candidates:

• Merge overlapping intervals.
• Reverse a linked list.
• Given a string, return 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.

Leadership Principles (behavioral) rounds

Amazon data engineer Leadership Principle (LP) rounds evaluate how you make decisions, handle setbacks, earn trust, and take ownership. LP questions also appear in every technical round, and each interviewer typically asks 2-3 behavioral questions.

Amazon's hiring team decides which Principles matter most for the role, and data engineer loops frequently test Customer Obsession, Dive Deep, Deliver Results, Ownership, and Frugality. Grounding any answer in customer impact strengthens it, even when the question targets a different Principle.

Interviewers ask follow-ups for every story. Prepare the metrics, trade-offs, and hindsight behind each one before the loop.

Interviewers look for:

• Structured storytelling: STAR answers with measurable impact
• Customer-first reasoning: Decisions framed around user needs and business outcomes
• Ownership: Examples of driving results through ambiguity and owning your mistakes
• Trust and conflict resolution: How you earn trust, handle disagreement, and learn from failure
• Depth under questioning: Detailed metrics, trade-offs, and lessons you can explain when asked about any part of a story

"Why Amazon?"

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

Connect a personal experience to Amazon's customer-focused operating model. An example of building a data product from user feedback, or iterating on one after direct customer input, demonstrates Customer Obsession without naming the Principle.

Recently asked questions

Here are some real interview questions reported by candidates:

• Tell me about your most challenging data project and how you handled it.
• Tell me about a time you disagreed with someone and how you resolved it.
• How do you set expectations on a project?
• What product that you led are you most proud of, and why?
• Tell me about a decision that caused a failure. What did you learn?

Bar Raiser round

In the Amazon data engineer loop, the Bar Raiser is an objective interviewer from outside the hiring team who can block a hire that doesn't meet Amazon's bar. You may not be told which interviewer is the Bar Raiser, so bring the same depth to every round.

After the loop, the Bar Raiser leads the debrief and guides the interviewers to a consensus hiring decision. The Bar Raiser round focuses mostly on Leadership Principles, and the Bar Raiser challenges your assumptions by returning to the same story with additional questions.

Bring your most detailed stories and the business outcome behind each decision.

Interviewers look for:

• Ownership: Decisions you made and owned, with clear outcomes
• Trade-off reasoning: Why you chose one approach and what it cost
• Consistency under follow-up: Stories that stay specific as the interviewer challenges your assumptions
• Hindsight: What you'd change and what you learned

How to prepare for the Amazon data engineer interview

1. Build one warehouse design you can defend at scale: Model the facts and dimensions of a business you know, then answer your own follow-ups on grain, slowly changing dimensions, primary keys, partition keys, and lookups at production volume. Kimball-style dimensional modeling covers the fundamentals Amazon interviewers test.
2. Build a Leadership Principles story bank: Prepare 5-6 core STAR stories that each answer more than one Principle, prioritizing Customer Obsession, Dive Deep, Deliver Results, Ownership, and Frugality. Give every story a metric, a trade-off, and a lesson, then add 1 to 2 backup anecdotes for the Principles the team emphasizes.
3. Prepare a walkthrough of every project on your resume: Cover the architecture, data volumes, metrics, and business impact of each one. Senior candidates should add the AI and learning initiatives they've led.
4. Practice SQL with Amazon-specific patterns: Work through window functions, CTEs, Top-N queries, consecutive-day logic, rolling aggregations, and month-over-month growth. Focus on fundamentals and on explaining your reasoning, the two areas Amazon interviewers question most.
5. Map AWS services to each pipeline stage: Know which of S3, Kinesis, Glue, EMR, Redshift, and DynamoDB you'd use for ingestion, storage, transformation, and serving. Be ready to explain how each choice handles late data and retries.
6. Run timed mock interviews: Simulate the full loop with peer mock interviews that combine SQL, data modeling, and behavioral questions in one session. For targeted feedback on your stories and designs, book expert coaching.

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

Amazon Data Engineer II job descriptions typically list 3+ years of data engineering experience, and some teams ask for 5+. Core strengths include data modeling, warehousing, and ETL; SQL and Python fundamentals; AWS services such as Redshift, S3, Glue, EMR, Kinesis, Firehose, and Lambda; and experience with NoSQL or distributed data systems.

Some 2026 postings also list generative AI experience as a plus.

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 modeling interview course
• Data pipeline design interview course
• SQL interview course
• Amazon Leadership Principles interview course
• Top SQL data engineering interview questions
• Real Amazon interview experiences

FAQs about the Amazon data engineer interview

What is the Amazon data engineer interview like?

Amazon's data engineer interview combines SQL, data modeling, ETL design, coding, and system design with Leadership Principle questions in every stage. The technical focus varies by hiring team, and data modeling prompts often center on Amazon businesses such as Prime. A Bar Raiser from outside the hiring team evaluates every candidate in the onsite loop and can block a hire.

Does the Amazon data engineer interview include an online assessment?

Some Amazon data engineer teams start with an online assessment before the technical phone screen, and others skip it. The assessment covers SQL fundamentals and a Python or coding challenge, and some versions add multiple-choice questions on incorrect queries. Ask your recruiter whether your loop includes one.

Can you use AI tools during the Amazon data engineer interview?

Amazon doesn't allow candidates to use generative AI tools during interviews unless the interviewer explicitly permits it, and unauthorized use can lead to disqualification. Amazon has also introduced AI-assisted coding assessments and, with candidate consent, interview recording and transcription, mainly for software engineering roles. Prepare to write SQL and Python without AI assistance.

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 with scheduling, whether your loop includes an online assessment, and how much time the team takes between rounds.

What SQL questions does Amazon ask data engineers?

Amazon asks data engineers SQL questions built on window functions, ranking, aggregations, and consecutive-day logic, usually set in retail, order, or employee data. Examples include finding the top 3 unique salaries per department and identifying users with consecutive daily activity. Interviewers then follow up on query performance for large tables.

How does the Amazon data engineer interview differ by level?

Amazon hires data engineers at L4 (Data Engineer I), L5 (Data Engineer II), and L6 (Senior Data Engineer), and raises design and scope expectations at each level. Senior and staff candidates should expect layered data models and behavioral questions about products and initiatives they've led.

Interns complete one round of 2 interviews, and new graduates complete a final round of 3-4 interviews that mix technical and behavioral questions. Both focus on SQL, standard data structures challenges, and Leadership Principles.

Candidates who miss the bar for their target level may receive an offer to complete a shorter loop of 2-3 interviews for a lower level.

How should I prepare for an Amazon data engineer interview?

Prepare for the Amazon data engineer interview by splitting your time between Leadership Principles and technical prep. Build structured stories for Customer Obsession, Dive Deep, and Deliver Results first. On the technical side, prioritize one defensible warehouse design, then SQL window functions, CTEs, and query optimization.

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 I): ~$143K
• L5 (Data Engineer II): ~$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 2 years.