Amazon Data Analyst interview typically runs 4-6 rounds: recruiter screen, online assessment, technical screen, onsite loop, and bar raiser. It usually takes 2-3 months and is highly structured, with heavy emphasis on SQL and Leadership Principles.
$113K
Avg. Base Comp
$170K
Avg. Total Comp
4-6
Typical Rounds
3-8 weeks
Process Length
We’ve seen Amazon consistently optimize for practical analysts who can think in business terms, not just query well. Across candidate experiences, SQL shows up early and often, but it’s rarely isolated from judgment: one candidate was asked to reason through transaction tables and join behavior, another had to organize data across multiple databases, and others were pressed on KPIs, churn, missing data, and how their analysis would change a business decision. Even when the technical questions were straightforward, interviewers kept pushing for clarity, scale, and impact. That lines up with the question set too — from Damaged Televisions Shipment Investigation to Success Measurement and Out of Stock Inventory, the pattern is less “solve a puzzle” and more “show us you can operate in Amazon’s world.”
A recurring theme is that Amazon cares a lot about how you explain your thinking under pressure. Multiple candidates reported structured, repetitive behavioral prompts tied to Leadership Principles, and the strongest experiences came from people who had several polished stories ready to adapt. We also noticed that interviewers often probe for specifics that reveal whether you’ve actually done the work: customer service, logistics, AWS basics, BI tools, data pipelines, and even how you handle feedback or disagreement. The non-obvious make-or-break factor here is not raw technical depth alone; it’s whether you can stay concise, confident, and concrete while switching between SQL, business reasoning, and Amazon-style ownership language. Candidates who treated it as a pure SQL interview often felt surprised by how much the company wanted evidence of judgment and communication.
Synthetized from 11 candidates reports by our editorial team.
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Featured question at Amazon
Write a query that returns all neighborhoods that have 0 users.
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|---|---|
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| Rolling Bank Transactions | |
| Customer Orders | |
| Comments Histogram | |
| Closest SAT Scores | |
| Top Three Salaries | |
| Monthly Customer Report | |
| Compute Deviation | |
| Experiment Validity | |
| Download Facts | |
| Random SQL Sample | |
| Button AB Test | |
| Subscription Overlap | |
| Month Over Month | |
| Prime to N | |
| Paired Products | |
| Upsell Transactions | |
| Swipe Precision | |
| Top 3 Users | |
| Bank Fraud Model | |
| Exam Scores | |
| Weekly Aggregation | |
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| Rolling Average Steps | |
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| Completed Shipments | |
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Synthesized from candidate reports. Individual experiences may vary.
The process often begins with an HR or recruiter screen focused on motivation, background, and fit for the Data Analyst role. Recruiters typically explain the loop, set expectations, and may coach candidates on Amazon-style behavioral answers and Leadership Principles.
Many candidates reported an assessment before live interviews, usually heavier on SQL than expected. It can include SQL multiple-choice questions, SQL coding problems, and in some cases Python or statistics questions.
The next step is usually a live technical interview, often on Chime, with a strong emphasis on SQL. Candidates were asked joins, transaction-style problems, row counts for different join types, and practical analytics questions; some screens also included basic Python, visualization tools, cloud/AWS fundamentals, or a small behavioral question at the end.
This round checks whether you understand the analyst role and can explain your past work clearly. Interviewers often focus on resume walkthroughs, project impact, communication style, and how your experience maps to Amazon’s Leadership Principles.
The final stage is typically a virtual onsite loop with multiple back-to-back interviews. Reported rounds included SQL coding, analytical problem-solving, behavioral interviews, and conversations with a PM or other cross-functional stakeholders; some candidates also had questions about customer service, logistics, AWS, BI tools, data pipelines, or database concepts.
Some loops end with a Bar Raiser interview that is heavily behavioral and leadership-principles driven. Candidates should expect structured STAR questions, ownership and judgment prompts, and a strong focus on Amazon culture fit and communication.