Amazon Data Engineer interview typically runs 3-6 rounds: recruiter screen, SQL assessment, technical phone screen, hiring manager panel, and onsite. It usually takes 2-6 weeks and is highly Leadership Principles-driven.
$123K
Avg. Base Comp
$220K
Avg. Total Comp
4-6
Typical Rounds
2-5 weeks
Process Length
We've seen a very consistent pattern in Amazon Data Engineer interviews: the team cares less about whether you can describe a pipeline and more about whether you can defend why it mattered. Multiple candidates reported heavy follow-up on metrics, ownership, and downstream impact — not just “I built a dashboard,” but so what? Did it change a decision, lift a funnel, or expose a real problem? That same theme shows up in the deep dives on unified data sources, ETL execution, and stakeholder alignment, where interviewers kept pushing on who owned the metric, where the data lived, and how the work improved the process.
Another recurring theme is that Amazon treats DE as a practical engineering role, not a narrow SQL role. Our candidates report SQL questions that are often framed as real business scenarios — joins, deduplication, ranking, sales aggregation, and product adoption analysis — alongside questions about data modeling, indexing, pipeline design, and even infrastructure basics in some teams. The non-obvious part is that the bar shifts with the team: some loops stay close to warehouse and BI work, while others go much deeper into operational systems, CDC, schema evolution, and distributed design. If there’s one thing that makes or breaks candidates here, it’s whether they can connect technical choices to business outcomes with enough specificity to survive the back-and-forth.
Synthetized from 13 candidates reports by our editorial team.
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Real interview reports from people who went through the Amazon process.
I had one online assessment and three virtual interview rounds. The online assessment covered data engineering fundamentals, two SQL questions, and situational questions based on data engineering production cases.
The first virtual round included four easy SQL questions based on a use case, plus a system design case for a real-time production pipeline. They asked about data skewness and how to tackle it.
The second round focused on my resume and projects, along with data engineering fundamentals such as the difference between indexing and partitioning and normalization.
The third round included two DSA questions, followed by about half an hour discussing my projects and leadership examples.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
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Synthesized from candidate reports. Individual experiences may vary.
A recruiter or HR partner reaches out to review your background, role fit, and basic expectations. This call is usually non-technical, but some candidates reported light discussion of SQL, data modeling, or resume details.
Many candidates start with an Amazon-hosted SQL assessment or take-home-style screening. The questions are often scenario-based and focus on joins, aggregations, window functions, deduplication, database security, and product analytics.
This round typically mixes live SQL with Python or coding fundamentals, and sometimes data modeling or basic engineering concepts. Interviewers may also probe your past projects and ask behavioral questions tied to Amazon Leadership Principles.
A hiring manager, sometimes joined by a BI engineer or data engineer, conducts a deeper behavioral and project deep-dive. Expect multi-part questions about end-to-end technical programs, cross-functional alignment, handling data from multiple sources, and the business impact of your work.
For many candidates, the process continues into a virtual loop with several interviews covering SQL, Python, system design, data modeling, and Leadership Principles. Rounds can include practical design problems such as CDC pipelines, distributed schedulers, dashboard performance, indexing, or large-scale data modeling.
The last round is often heavily focused on Amazon Leadership Principles and ownership. Interviewers dig into your decisions, tradeoffs, conflict handling, and measurable outcomes, frequently pushing for specifics on impact and metrics before making a final decision.