Amazon Data Analyst interview typically runs 4–6 rounds: online assessment, recruiter screen, technical phone screen, onsite loop, and bar raiser. The process spans 2–3 months and is distinguished by its dual focus on SQL proficiency and Amazon Leadership Principles behavioral questions.
$88K
Avg. Base Comp
$177K
Avg. Total Comp
4-6
Typical Rounds
3-8 weeks
Process Length
What catches candidates off guard at Amazon is that they're effectively being evaluated on two parallel tracks simultaneously — technical execution and behavioral evidence — and a weak showing on either one is enough to end the process. We've seen SQL come up at nearly every stage, from the online assessment through the final loop, but the questions rarely stop at syntax. Multiple candidates reported being asked to reason through transaction-level problems, explain join behavior with sample tables, and then immediately pivot to a STAR story about a time they influenced a stakeholder with data. The ability to switch registers quickly — from query logic to business narrative — is what separates candidates who advance from those who don't.
A recurring theme across experiences is that Amazon interviewers probe for specificity in ways that feel relentless. One candidate described the behavioral portion as the interviewer "kept drilling deeper" to separate personal contributions from team outcomes. Another noted that having "several polished stories ready to adapt" was the key differentiator in the bar raiser round. The question set reinforces this: prompts like Damaged Televisions Shipment Investigation and Decreasing Payments aren't abstract puzzles — they're designed to see whether you instinctively frame problems around customer impact and operational ownership, which is Amazon's actual working language.
The non-obvious make-or-break factor is scale and specificity in your examples. Candidates who received offers consistently mentioned being prepared with metrics, concrete outcomes, and multiple Leadership Principle stories that could flex to different prompts. Those who didn't get offers often noted that the behavioral side felt harder than expected, or that they were surprised by how much the process cared about AWS familiarity, BI tooling, and data quality judgment alongside core SQL. Treat this as a role that demands both a working analyst and a mini-product thinker who can defend every decision out loud.
Synthesized from 18 candidate reports by our editorial team.
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Real interview reports from people who went through the Amazon process.
The process was pretty straightforward overall, but it started with an online assessment that set the tone. The OA had SQL questions, some multiple-choice questions, and one DSA problem. The SQL part was the main focus, and the coding question was more about basic Python syntax than anything deep. A few weeks later, I moved on to a live round that was one LeetCode medium question plus about 15 minutes of behavioral questions. The coding problem I got was Max Consecutive Ones III, so it was definitely manageable if you were comfortable with sliding window style problems.
The technical interviews themselves were mostly SQL-heavy. I was asked things like the order of execution of SQL commands such as select, from, where, and group by, and I also had to explain the differences between the three types of joins. Other questions stayed at a basic to medium level around joins, group by, NULL values, window functions, and general SQL theory. There were also some resume-based questions and a few questions about projects I had worked on, plus basic Python knowledge with pandas and numpy. One part that stood out was the work-style assessment, where they asked me to sort activities for a workday based on priority, importance, duration, and what someone had just told me to do. The personality-style section was more about picking traits on a spectrum. Overall it felt easy to moderate, with the main challenge being staying sharp on SQL fundamentals rather than solving anything tricky. I didn’t get the offer in the end, so I’d say the best prep is to drill SQL order of execution, joins, group by, NULL handling, and a few common window function patterns, then make sure you can talk through your projects clearly.
Prep tip from this candidate
Drill SQL order of execution, joins, group by, NULL handling, and window functions, since those came up repeatedly. Also be ready for a work-style assessment that asks you to prioritize tasks in a realistic workday scenario.
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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 | |
| Customer Orders | |
| Comments Histogram | |
| Closest SAT Scores | |
| Top Three Salaries | |
| Monthly Customer Report | |
| Compute Deviation | |
| Experiment Validity | |
| Download Facts | |
| Random SQL Sample | |
| Subscription Overlap | |
| Button AB Test | |
| Month Over Month | |
| Prime to N | |
| Paired Products | |
| Upsell Transactions | |
| Swipe Precision | |
| Top 3 Users | |
| Longest Streak Users | |
| Bank Fraud Model | |
| Exam Scores | |
| Identifying User Sessions | |
| Encoding Categorical Features | |
| Rolling Average Steps | |
| Weekly Aggregation | |
| Network Experiment Design | |
| Bagging vs Boosting | |
| Completed Shipments |
Synthesized from candidate reports. Individual experiences may vary.
The process typically begins with an HR or recruiter phone call focused on background, motivation, and role fit. Recruiters often explain the full loop structure, set expectations for each stage, and coach candidates on Amazon's Leadership Principles and STAR-format behavioral responses.
Many candidates complete an online assessment before any live interviews, covering SQL multiple-choice and coding problems, and sometimes basic Python or statistics questions. The SQL portion is heavier than most candidates expect, and some assessments also include a personality or work-style section.
A live technical interview, often conducted on Amazon Chime, with a strong emphasis on SQL including joins, aggregations, window functions, transaction-style problems, and edge cases. Some screens also touch on basic Python, visualization tools such as Tableau or QuickSight, cloud and AWS fundamentals, and one or two behavioral questions at the end.
This round evaluates whether you understand the analyst role and can clearly articulate the business impact of your past work. Interviewers focus on resume walkthroughs, project scope and outcomes, communication style, and how your experience maps to Amazon's Leadership Principles.
The final stage is a virtual onsite with multiple back-to-back interviews covering SQL coding, analytical problem-solving, experimentation design, and cross-functional conversations with stakeholders such as PMs. Some loops also include an Excel or BI tool assessment, and questions may span data pipelines, database concepts, AWS services, and metrics definition.
Many loops include a Bar Raiser interview that is heavily behavioral and Leadership Principles-driven, conducted by a senior Amazonian outside the immediate team. Candidates should expect structured STAR questions with deep follow-ups on ownership, judgment, customer obsession, and cultural fit.