Amazon Data Engineer candidates commonly report SQL and data modeling assessments, technical interviews mixing SQL or Python with behavioral questions, and Leadership Principles throughout the process.
$127K
Avg. Base Comp
$224K
Avg. Total Comp
2-5 rounds
Typical Rounds
2-5 weeks
Process Length
Amazon Data Engineer interviews reported here repeatedly combine hands-on data work with behavioral follow-ups. Candidates commonly describe an online assessment before interviews; its format varies from SQL-heavy questions to coding and work-style items. In reported technical conversations, SQL appears alongside Python, data modeling, and practical data-engineering fundamentals rather than as an isolated exercise.
Prepare to explain your reasoning, not only produce an answer. Candidates report SQL prompts involving joins, aggregations, CTEs, ranking functions, duplicate detection, and query performance. Modeling discussions have included warehouse and lake distinctions, OLTP versus OLAP, star and snowflake schemas, slowly changing dimensions, and schema choices for a scenario. Some accounts also include a scalable data-processing or ETL design discussion, with follow-up questions about tradeoffs and performance diagnosis.
Amazon Leadership Principles are a recurring part of the reported process. Interviewers may ask for detailed examples of ownership, difficult projects, collaboration, migrations, or decisions made under constraints, then probe for your individual actions and results. Build concise stories that let you explain the technical context and the choices you made.
The reports vary by team and level, so treat the sequence and mix of questions as a guide rather than a fixed script.
Synthesized from 33 candidate reports by our editorial team.
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Real interview reports from people who went through the Amazon process.
Five rounds: intro call, two coding sessions (DSA + ML coding), an ML system design round for a recommendation system, and a behavioral round. Coding was LeetCode medium. I withdrew after accepting another offer. Recruiters were responsive and the process moved quickly once I passed the phone screen.
Questions asked:
Walk me through a time you used data to influence a decision. I described a project where I found that a promotional email was cannibalizing organic purchases rather than driving incremental ones. I built a holdout analysis, presented the findings to the marketing team, and we redesigned the targeting criteria — which improved net revenue by 8%. The interviewer asked how I got stakeholder buy-in; I explained I framed it around business impact rather than methodology.
SQL: calculate 7-day rolling retention. I used a window function — COUNT(DISTINCT user_id) OVER (ORDER BY date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) — and explained that retention needs a cohort anchor date to be meaningful. They appreciated that I flagged the denominator issue (active users vs. registered users).
What metrics would you use to evaluate a new checkout flow? I proposed funnel conversion rate at each step, abandonment rate, time-to-purchase, and error rate. I also suggested segmenting by device type since mobile drop-off is typically much higher. They followed up asking how I'd design an experiment around this; I said I'd run an A/B test targeting users at cart stage with a minimum 2-week runtime to capture weekly purchase cycles.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Amazon
Write a query that returns all neighborhoods that have 0 users.
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| 2nd Highest Salary | |
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| Comments Histogram | |
| Closest SAT Scores | |
| Subscription Overlap | |
| Top Three Salaries | |
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| Random SQL Sample | |
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| Upsell Transactions | |
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| Monthly Customer Report | |
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| Address Schema | |
| Compute Deviation | |
| Identifying User Sessions | |
| Retailer Data Warehouse | |
| Permutation Palindrome | |
| Find the First Non-Repeating Character in a String |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report an assessment before interviews, often with SQL as a major component. Reported formats include practical SQL questions, multiple-choice or scenario questions, coding tasks, and work-style items; one candidate described a two-and-a-half-hour SQL-and-Python assessment.
Candidates report technical conversations that may combine live SQL or Python with modeling and data-engineering fundamentals. Examples include joins, CTEs, ranking functions, warehouses, OLTP versus OLAP, dimensional schemas, and explaining how a model supports the required queries.
Later interviews may test data-application or pipeline design alongside practical technical reasoning. Candidates describe discussing scalable data processing, ETL orchestration, database choices, performance diagnosis, and coding problems while talking through tradeoffs.
Behavioral questions are reported throughout the process and may be dedicated interviews as well. Candidates describe prompts about ownership, collaboration, difficult projects, migrations, and business results, with follow-ups asking why they made particular technical decisions.
