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Amazon · Data Analyst

Amazon Data Analyst Interview Questions (2026): process, questions and answers

Updated 23 October 2026 · prep guide

Amazon Data Analyst interviews usually focus on SQL depth, dashboard thinking, clean data handling, and how you turn data into a business decision. Use this page to practise short, clear answers and to review the kind of technical and scenario questions candidates commonly report.

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How the Amazon Data Analyst interview usually works

  1. Resume and experience review: Interviewers often start by checking whether your past work fits analytics in a product company. Be ready to explain datasets, metrics, tools, and the business impact of your work in simple terms.
  2. SQL and data reasoning: You will usually face questions on joins, aggregations, window functions, filtering, and data quality. Expect follow-ups that test whether you can reason about edge cases and explain why your query is correct.
  3. BI and reporting: Candidates commonly get questions on Excel, Power BI, or Tableau, especially around metric definitions, drill-downs, and dashboard usability. They may ask how you would design a report for an operations or business team.
  4. Statistics and business case discussion: You may be asked to interpret trends, compare groups, or explain whether a change is meaningful. The strongest answers connect the numbers to a customer, process, or revenue outcome.

Technical questions and answers

How do you write a SQL query to find the top 3 products by revenue in each category?

I would first aggregate revenue by category and product_id, then use ROW_NUMBER() partitioned by category and ordered by total revenue descending. After that, I would filter to rows where the rank is 3 or less. This is better than a plain GROUP BY because I need a top-N result within each category, not one overall list. I would also check tie handling, because business users may want either exactly 3 rows or all tied products.

What is the difference between GROUP BY and window functions?

GROUP BY collapses rows and returns one row per group, so it is useful for totals, averages, and counts. Window functions keep the original rows and add calculated values beside them, such as a running total or rank. I use GROUP BY when I need summary output, and window functions when I need analysis at row level plus context. In reporting, window functions are especially helpful when I need both detail and comparison in one query.

How would you calculate month-over-month growth in SQL?

I would aggregate the metric by month first, then use LAG() to bring in the previous month’s value. After that, I would compute growth as (current_month - previous_month) / previous_month. I would protect against division by zero and missing months. If the dataset has gaps, I may need a calendar table so the comparison is truly month to month. That makes the result more accurate for business reporting.

How do you check whether a dataset has duplicate records before analysis?

I would define the business key first, because duplicates depend on what should be unique. Then I would use GROUP BY with COUNT(*) to find repeated key combinations, and inspect whether duplicates are true repeats or valid multiple events. If needed, I would compare the row count before and after deduplication to understand impact. In an analytics setting, I would also ask whether duplicates should be removed, flagged, or retained for traceability.

How do you explain and use an INDEX MATCH lookup in Excel?

INDEX MATCH finds a value by matching a key in one range and returning the corresponding value from another range. I like it because it is more flexible than VLOOKUP, especially when the lookup column is not on the left. It also avoids breaking when columns are inserted. In practice, I use it to map IDs to names, categories, or regions. I would still verify that lookup keys are unique and that missing values are handled cleanly.

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How do you build a simple KPI card or summary in Power BI or Tableau?

I would start by defining the metric clearly, then create the calculation in the model or as a measure. Next, I would place it on a clean dashboard with a clear title, date context, and a comparison such as last period or target. I would keep the card simple, with one message per visual. For a product company, I would also add a filter for time or region so the user can quickly see the metric in context.

What is the difference between correlation and causation?

Correlation means two variables move together, but it does not prove one causes the other. Causation means one variable directly affects the other, which usually requires stronger evidence, such as an experiment or careful analysis of confounding factors. In business analysis, this matters a lot because a trend can look convincing without being actionable. I would say, for example, that higher traffic and higher sales may be related, but traffic alone may not be the cause.

How do you handle missing values in an analysis, and when would you not fill them?

I first find out why values are missing, because the reason affects the treatment. If the missing data is small and random, I may remove those rows or impute carefully, depending on the metric. If missingness itself is meaningful, I keep it and report it as a separate category. I would not blindly fill every blank, because that can distort averages, conversion rates, or trend analysis. The key is to protect business accuracy.

How would you compare two groups to see if a change is significant?

I would start by checking the metric type, sample size, and whether the data is roughly comparable across groups. For means, I may use a t-test; for proportions, I may use a proportion test. I would also look at practical impact, not just statistical significance, because a tiny but significant change may not matter to the business. If possible, I would segment the result to see whether the change is concentrated in a specific user group or region.

How do you design a SQL query that avoids double counting when joining transactional tables?

I would first understand the grain of each table, because double counting usually happens when one row joins to many rows unexpectedly. If needed, I would aggregate one side before joining, or join on a truly unique key. I would validate by comparing counts and totals before and after the join. For example, if order-level data joins to item-level data, I must be clear whether revenue should be counted per order or per item. Grain control is the main safeguard.

Scenario questions

A dashboard shows a sudden drop in completed orders for one region. How would you investigate it?

I would first confirm whether the drop is real by checking the source data, refresh timing, and whether the dashboard filter changed. Then I would break the metric down by device, channel, product, and day to isolate where the drop started. I would also compare it with traffic, payment success, and cancellations to see if the issue is demand or conversion related. My answer should show a structured investigation, not a guess.

Business asks for a weekly report on late deliveries. What fields and checks would you include?

I would include order ID, promised delivery date, actual delivery date, region, fulfillment type, carrier, and a late flag. I would define lateness clearly, for example, delivered after the promised date or outside the service level. I would also check for missing dates, duplicate orders, and timezone issues. In my answer, I would explain that a good operational report must have a clear metric definition, clean joins, and a way to segment delays by root cause.

You find that two reports show different numbers for the same business metric. What would you do?

I would compare the metric definition first, because the difference is often caused by filters, time windows, or the level of aggregation. Then I would trace both reports back to the source tables and confirm whether they are using the same business rules. I would check refresh timing, excluded rows, and any manual adjustments. My response should show that I verify definitions before debugging data, because mismatched logic is a common reason for conflicting numbers.

HR and behavioural questions

Tell me about a time you found a data issue.

Use STAR: situation, task, action, result. Keep the focus on how you detected the issue, how you validated the root cause, and how you communicated the impact. A short sample answer is: I noticed a report was overstating conversions because duplicate events were being counted. I traced the issue to a join problem, fixed the logic, and shared the corrected metric with the team. The result was a more reliable dashboard and fewer follow-up questions.

How do you handle a stakeholder who wants a quick answer, but the data is incomplete?

Show that you can be honest and practical. Structure the answer as: what is known, what is uncertain, and what safe next step you can take. A strong sample answer is: I would explain that the current data can support a directional view, but not a final conclusion. I would share the best available estimate, call out the gap, and propose the next check or data source. That shows transparency without blocking progress.

Describe a time you had to work with a difficult cross-functional partner.

Keep it professional and outcome-focused. Explain the disagreement, how you aligned on the business goal, and what you changed in your communication. A sample answer is: A partner wanted a report with too many metrics, so I asked what decision they needed to make. We narrowed the scope to three KPIs, and the report became easier to use. I learned that asking about the decision, not just the data request, helps reduce friction.

Why do you want to work in analytics at a product company like Amazon?

Connect your interest to scale, customer impact, and decision-making. A good sample answer is: I enjoy turning data into actions that improve customer experience and operations. In a product company, analytics has direct business impact because the numbers influence product, supply chain, and service decisions. I also like working in an environment where accuracy, ownership, and clear communication matter. Keep the answer specific and avoid generic statements about liking numbers.

Your last 48 hours before the interview

  1. Revise your last 3 projects and prepare a 60 second summary for each using problem, method, result.
  2. Practise writing SQL for top-N, ranking, and trend questions without looking at notes.
  3. Review JOIN, GROUP BY, LAG(), ROW_NUMBER(), and basic date handling until you can explain them aloud.
  4. Prepare one Excel example and one dashboard example, including the business problem and the metric definition.
  5. Rehearse 4 behavioural stories using STAR, with one story each for issue detection, conflict, ambiguity, and impact.
  6. Do a final mock round where you answer out loud in short sentences and stop yourself from overexplaining.

Common mistakes to avoid

Frequently asked questions

What SQL level is usually expected for an Amazon Data Analyst interview?

Candidates commonly report needing solid intermediate SQL. You should be comfortable with joins, aggregation, subqueries, CTEs, and window functions, plus explaining why a query is built a certain way. The interview often checks both correctness and reasoning.

Should I expect Excel or Power BI or Tableau questions?

Yes, usually at least one part of the interview covers BI or spreadsheet work. Be ready to explain how you build a clean report, validate a metric, and make a dashboard useful for a business user. The focus is often more on logic than on fancy formatting.

How should I answer business case questions in this interview?

Start by clarifying the goal, then define the metric, the segments, and the time frame you would analyse. After that, explain what action you would recommend based on the result. A clear structure matters more than a perfect answer.

How can I avoid sounding too theoretical?

Use short examples from actual reporting, cleaning, or analysis work. Say what data you used, what problem you found, and what changed after your analysis. Interviewers usually respond well to practical answers that show ownership and business sense.

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This is an independent preparation guide based on commonly reported interview patterns. The process varies by team, location and year. Interview AI Helper is not affiliated with Amazon.

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