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.
$85K
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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Real interview reports from people who went through the Amazon process.
It started with an online assessment that was heavier on SQL than I expected: a set of SQL multiple-choice questions plus two SQL coding problems. After that came a 75-minute technical phone screen that stayed almost entirely on SQL, with just a couple of basic visualization questions and one behavioral question at the end. The SQL screen ramped up in difficulty as I went, starting with a joins question and then pushing into edge cases. I chose my own test cases, and the interviewer was actually pretty helpful with hints when I got stuck, which made it feel more collaborative than adversarial.
The onsite was virtual and split into five rounds over about five hours: a manager round, an analytical problem-solving round, a SQL coding round, a round with a PM, and a bar raiser round. The recruiter did a good job laying out the themes ahead of time and even shared the leadership principles for each round, which was really useful. I’m glad I took the recruiter prep call seriously because it gave me enough detail to prepare the behavioral side properly. The day was intense, though, and by the end I was pretty drained. If I could do it again, I’d definitely prefer the two-day format instead of packing everything into one long stretch, since that would have given me more breathing room between rounds. My main takeaway is to practice mock interviews around the specific themes they give you, especially SQL under pressure and Amazon-style behavioral questions. I ended up getting the offer, and the process felt thorough but fair.
Prep tip from this candidate
Practice SQL coding under time pressure, especially joins and edge cases, and do mock interviews tailored to the specific onsite themes the recruiter shares. Don’t skip the recruiter prep call, since it gives you the leadership-principle focus for each round.
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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.
| Question | |
|---|---|
| 2nd Highest Salary | |
| Rolling Bank Transactions | |
| Comments Histogram | |
| Customer Orders | |
| Closest SAT Scores | |
| Top Three Salaries | |
| Monthly Customer Report | |
| Download Facts | |
| Compute Deviation | |
| Experiment Validity | |
| Random SQL Sample | |
| Button AB Test | |
| Month Over Month | |
| Paired Products | |
| Subscription Overlap | |
| Prime to N | |
| Upsell Transactions | |
| Swipe Precision | |
| Top 3 Users | |
| Bank Fraud Model | |
| Exam Scores | |
| Weekly Aggregation | |
| Encoding Categorical Features | |
| Rolling Average Steps | |
| Network Experiment Design | |
| Completed Shipments | |
| Bagging vs Boosting | |
| Average Quantity | |
| Booking Regression |
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.