Amazon Business Intelligence candidates commonly report SQL assessments and live-query work alongside Leadership Principles, business metrics, experimentation, dashboards, and project-depth discussions.
$132K
Avg. Base Comp
$181K
Avg. Total Comp
3-6 rounds
Typical Rounds
3-5 weeks
Process Length
Amazon Business Intelligence interviews reported here combine practical analysis with close questioning about how candidates make business decisions. SQL is the most repeated technical theme: candidates describe online assessments, live exercises, and questions involving joins, aggregations, CTEs, window functions, ranking, and query behavior. Some accounts also include dashboard or visualization prompts, such as investigating a metric decline, selecting a chart, or explaining findings to a non-technical stakeholder.
Business judgment is a clear companion to the technical work. One recent account focused entirely on business acumen and data visualization, including an A/B-test trade-off between revenue and customer satisfaction, metric selection, guardrails, and presentation choices. Prepare to explain why a metric matters, what could make it misleading, and how you would communicate conflicting results—not simply name a KPI.
Leadership Principles and past-project depth recur across screens and later interviews. Candidates report follow-up questions that probe their individual contribution, ambiguity, root-cause analysis, ownership, and outcomes. Build STAR stories with concrete decisions and results, then practice adapting them to different prompts. Reported formats vary by team and level, so treat the sequence below as a preparation framework rather than one fixed path.
Synthesized from 43 candidate reports by our editorial team.
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Real interview reports from people who went through the Amazon process.
The round was taken by an L5 Business Analyst, and the entire focus was on two things: business acumen and data visualization. No coding, no SQL grilling — just straight-up "do you actually think like a BA?" energy.
What came up:
She dug into A/B testing — not just "what is it" textbook stuff, but how you'd actually design one, what you'd watch for, how you'd call a winner Metrics — the kind of questions where she wanted to see if you can pick the right metric for a business problem, not just rattle off a list of KPIs Data visualization scenarios — how would you present findings, what chart would you pick and why Where it felt solid:
Business acumen questions felt like home turf — connecting data decisions back to business impact is something that clicks naturally Where the sweat started:
A/B testing deep-dives can get tricky fast — once they start pushing on statistical significance, sample sizes, and edge cases, it stops being a casual conversation real quick
Questions asked: One of the key questions was around A/B testing — something like: "If you launched a new feature and ran an A/B test, but the test group showed higher revenue but lower customer satisfaction, what would you do? Which metric would you prioritize and why?" She also pushed on metrics selection — not just "name some KPIs," but scenario-driven: how would you measure success for X, what's your north star metric, and what guardrail metrics would you watch alongside it? On the data visualization side, it was about judgment — when would you choose a bar chart vs. a line chart, and how would you present conflicting data to a non-technical stakeholder?
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Topics based on recent interview experiences.
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| Question | |
|---|---|
| Rolling Bank Transactions | |
| Customer Orders | |
| Comments Histogram | |
| Closest SAT Scores | |
| Subscription Overlap | |
| Empty Neighborhoods | |
| Top Three Salaries | |
| Monthly Customer Report | |
| Experiment Validity | |
| Average Quantity | |
| Manager Team Sizes | |
| Month Over Month | |
| Button AB Test | |
| Flight Records | |
| Upsell Transactions | |
| Top 3 Users | |
| Longest Streak Users | |
| Merge Sorted Lists | |
| Project Pairs | |
| Compute Deviation | |
| Address Schema | |
| Rolling Average Steps | |
| Retailer Data Warehouse | |
| Download Facts | |
| Total Time in Flight | |
| Daily Retention Summary | |
| Monthly Product Sales | |
| Group Success | |
| Random SQL Sample |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report an initial online assessment in some processes, with SQL problems or SQL multiple-choice questions and working-style or leadership questions. Reported SQL content includes joins, output reasoning, window functions, ranking, pivoting, and practical query scenarios; the exact assessment format varies.
Candidates report recruiter or phone conversations that may cover background and prior projects, plus a mix of behavioral and technical questions. Some screens include live or whiteboard-style SQL, while others include Leadership Principle questions or a discussion of BI experience.
Candidates report technical rounds centered on writing and explaining SQL, including joins, aggregations, CTEs, and window functions. Depending on the team, the discussion may extend to experiment design, metric selection, dashboard design, data visualization, or diagnosing a business-metric change.
Candidates report later rounds that mix technical work with detailed discussion of past projects and Leadership Principles. Some describe a dedicated behavioral or Bar Raiser-style conversation; others report additional SQL, ETL, visualization, or business-acumen discussion. The number and composition of final interviews vary.
