Amazon SQL Interview Questions: Top Prep Guide & Sample Answers

From consumer analytics to AWS infrastructure, SQL is everywhere at Amazon. Whether you’re building dashboards, troubleshooting data pipelines, or running product experiments, your ability to write clean, efficient queries will be tested early—and often.

Why SQL Can Make or Break Your Amazon Offer

SQL isn’t just another checkbox in Amazon’s hiring process—it’s foundational. According to 2025 data, SQL appears in 42% of FAANG data interviews, and Amazon is no exception. You’ll encounter SQL assessments in interviews for roles like Data Analyst, BI Engineer, and even cross-functional roles like Google’s Data Engineering track.

What makes Amazon SQL interview questions uniquely difficult is their business-first framing. You’re not just writing queries—you’re solving ambiguous problems, working with non-standard schemas, and thinking through performance at petabyte scale. In short, Amazon wants to know: can you turn messy data into trusted insight?

This guide breaks down exactly how to do that. From SQL interview questions that test edge-case logic to optimization prompts that mimic real AWS workloads, we’ll walk through the most common formats and how to approach them.

Amazon SQL questions are designed to test your ability to think like a data owner, not just a query writer. You’ll be expected to clean, filter, join, and transform real-world tables—then defend your approach under time pressure. These interviews prioritize clarity, performance, and real-life edge-case thinking.

You’ll often see SQL interview questions amazon teams ask split across three main types. Let’s explore each below.

Conceptual SQL Questions

These test your foundational understanding of how SQL works behind the scenes. Amazon might ask you to explain the difference between INNER JOIN and LEFT JOIN in the context of null-heavy datasets or probe how you’d handle schema changes in a production pipeline.

Expect questions like:

• What happens if you GROUP BY a column that includes null values?
• How does a WHERE clause differ from a HAVING clause?
• Why might a COUNT(DISTINCT user_id) behave unexpectedly?

Conceptual questions often appear in early interviews—especially at L4 for Data Analysts or entry-level BI Engineers—where data literacy is key.

Applied SQL Questions

These are the bread-and-butter of amazon SQL questions. You’ll be asked to solve actual business scenarios using SELECT statements, aggregations, window functions, and CTEs. These questions often mirror tasks Amazon teams do daily.

Examples include:

• Calculate the 7-day rolling average of user signups.
• Identify the top 3 product categories by GMV in the last quarter.
• Detect users who churned within 14 days of registration.

In these cases, it’s not just about getting the right result—it’s about demonstrating structured thinking, clarity of code, and handling ambiguity in schema or requirements.

Optimization Questions

At Amazon’s scale, performance is never an afterthought. Optimization-style SQL interview questions Amazon may ask you to refactor an inefficient query, reduce I/O load, or explain trade-offs in join strategies. These commonly appear at the L5+ level (especially in AWS or Global Analytics teams).

You might face tasks like:

• Rewrite a query to avoid a full table scan on a partitioned dataset.
• Choose between broadcasting a small dimension table vs. performing a shuffle.
• Refactor nested subqueries into CTEs for better readability and caching.

Your answer should reflect an understanding of distributed query execution—especially if working with Redshift, Presto, or Athena.

Before diving into practice problems, here’s a high-level summary of what to expect across basic SQL interview questions and beyond.

This section gives you a condensed mental model of what to expect across Amazon’s SQL interview rounds. Whether you’re tackling basic SQL interview questions or high-scale Redshift architecture prompts, use this guide to sharpen your prep across concepts, execution, and communication.

Difficulty Tiers – From SELECT to Scale

Amazon SQL interviews follow a layered difficulty model. Early-stage candidates, such as interns or entry-level analysts, face basic questions like filtering rows with WHERE, joining tables, or computing aggregates like COUNT. As you progress to mid-level roles, expect intermediate challenges that combine subqueries, window functions, and case-based logic. At the senior level, you’ll be pushed to handle scalable query design, optimize for cost/performance (especially in Redshift), and explain the why behind your decisions. Be ready to move from writing the query to defending its production-readiness.

Most-Asked SQL Functions at Amazon

While each role differs slightly, a few SQL functions come up again and again:

• Aggregates like COUNT, SUM, AVG, often with GROUP BY
• Joins — both inner and outer — are fundamental
• Window functions such as ROW_NUMBER(), RANK(), LAG() show up in ranking and cohort analyses
• Date functions like DATEDIFF() and DATE_TRUNC() for time-based slicing
• CASE statements for conditional logic within SELECTs

Candidates should not only practice using these but also understand when they’re preferable over alternatives (e.g., using ROW_NUMBER() over RANK() when uniqueness matters).

Top Pitfalls to Avoid

Acing an Amazon SQL interview means steering clear of easy mistakes. Here are three that catch candidates off guard:

1. Using WHERE when HAVING is required — a classic trap in grouped queries.
2. Not partitioning in window functions — skipping PARTITION BY can break the logic or skew results.
3. Misunderstanding NULL behavior — always clarify what counts as “missing” or “zero” in the dataset.

Also: double-check edge cases like ties in ranking, duplicate keys, and off-by-one errors in date windows.

Pro Tips That Give You an Edge

One of the fastest ways to stand out? Communicate how you think. Instead of just writing the final query, walk through your assumptions about schema, volume, and constraints. This shows real-world readiness.

Pro tip: Before writing any query, ask clarifying questions like, “Can I assume each order ID is unique?” or “Is the timestamp in UTC or localized?” — especially in mock interviews. It signals thoughtfulness, not hesitation.

And always test your query logic with a simplified example. Amazon values correctness over cleverness. Clear > clever, every time.

Basic SQL Interview Questions (0–2 Yrs)

This section focuses on basic SQL interview questions that show up most often for early-career roles. These include internships, entry-level Data Analyst roles, or L4-level Amazon Data Engineers. Each one tests your grasp of query fundamentals, from basic joins to aggregation logic.

You’ll find 8–10 real SQL interview questions, each paired with a brief explanation, structured answer, and a note on why Amazon asks it. These are foundational and should be second nature before you attempt more complex exercises.

1. How would you find all customers who placed fewer than three orders or spent under $500 in total?

   Use an aggregate query on the orders table that groups by user_id, counting orders and summing spend, then apply a HAVING clause such as COUNT(*) < 3 OR SUM(order_amount) < 500. Join the filtered IDs back to the users table to return names and other profile details. This exercise checks that you can combine two aggregate conditions in a single pass—an everyday need when segmenting light-versus-high-value shoppers on Amazon’s retail platforms.

2. Which users have been “Excited” about an ad campaign at least once and never recorded as “Bored”?

   Aggregate impressions by user_id, computing MAX(status = 'Excited') and MAX(status = 'Bored'). Retain only those rows where the first max is 1 and the second is 0. The problem tests Boolean logic inside aggregates, a handy trick for audience segmentation queries that fuel Amazon Ads reporting dashboards.

3. How would you calculate each Uber passenger’s average ride duration (in minutes) and list the results alphabetically by user ID?

   Derive ride length via TIMESTAMPDIFF(MINUTE, start_ts, end_ts) per ride, then average by passenger_user_id with GROUP BY. Sort by the user ID column to satisfy the ordering requirement. Interviewers verify that you can perform date arithmetic, handle potential NULL end times safely, and produce tidy output—skills that transfer directly to Amazon Logistics latency analyses.

4. Write a query that returns every neighborhood containing zero registered users. How would you do it?

   Perform a LEFT JOIN neighborhoods n ON n.id = u.neighborhood_id and filter rows where u.neighborhood_id IS NULL. This anti-join pattern is Amazon’s go-to test for detecting data gaps—think “which fulfillment zones lack Prime Now coverage?”

5. For each Wi-Fi network (SSID), what is the largest number of packets sent by a single device in the first ten minutes of 1 Jan 2022?

   Filter rows to timestamp ≤ ‘2022-01-01 00:10’, group by ssid, device_id, count packets, and then select the maximum device-level count per ssid using a window function or correlated sub-query. The question blends time filtering with “greatest-N-per-group,” similar to how AWS IoT teams summarize telemetry spikes.

6. How can you list, for every product and year, the maximum quantity any single order contained?

   Extract the year from the transaction date, group by that year and product_id, and apply MAX(quantity) as max_quantity. Sort results on year and product. This checks multi-level grouping and date extraction—frequent tasks in Amazon Retail Finance analyses.

7. Write a query that computes total spending per user and orders the list from highest to lowest spender.

   Summarize each user’s SUM(cost) in the transactions table, group by user_id, and ORDER BY total_cost DESC. Although straightforward, it reveals whether a candidate remembers to alias aggregates and produces clean, sorted leaderboards—reports that drive Amazon loyalty campaigns.

8. How would you compare the average daily downloads of free vs paying accounts—only for accounts with at least one prior download?

   Join accounts to downloads, enforce the “at least one download” rule via a WHERE EXISTS sub-query or pre-aggregated list, then group by is_paying, DATE(download_ts) and compute ROUND(AVG(downloads), 2). This question tests conditional filtering, multi-column grouping, and numeric formatting—exactly what Prime Video analysts do when judging feature uptake.

9. Which manager supervises the largest team, and how would you retrieve their ID?

   Self-join the employees table on manager_id, count direct reports per manager, ORDER BY COUNT(*) DESC LIMIT 1. It confirms self-join fluency and aggregation ordering—skills needed for Amazon org-chart capacity planning tools.

10. What SQL would you write to return the second-highest salary in the Engineering department (ignoring ties at the top)?

    Select distinct salaries in Engineering, order them descending, then OFFSET 1 LIMIT 1, or apply DENSE_RANK() partitioned by department and filter on rank = 2. Amazon loves this classic because it differentiates candidates who know window functions from those who resort to error-prone sub-queries.

Intermediate SQL Interview Questions (3–5 Yrs)

Now that you’ve mastered the basics, it’s time to level up. This section includes top SQL interview questions typically seen in Amazon interviews for mid-level roles like BI Engineer II or Data Scientist I. These prompts reflect deeper expectations around query performance, analytical logic, and real-world troubleshooting.

Grouped question categories:

• Optimization & Execution Plans – Diagnose slowdowns using EXPLAIN, ANALYZE, and logical plan analysis.
• Aggregations & Window Functions – Apply ranking, rolling averages, and funnel logic across partitioned datasets.
• Data-Quality Checks – Write queries that spot duplicates, schema mismatches, and null anomalies in production logs.

Expect themes like query optimization, SQL-style QA validation, and multi-layer aggregations. Many of these are also common SQL interview questions for data analyst candidates working in experimentation or reporting workflows.

1. Which pairs of projects have an end-date that exactly matches another project’s start-date?

   Self-join the projects table (p1.end_date = p2.start_date AND p1.id <> p2.id), deduplicate symmetric matches with p1.id < p2.id, and return both project IDs plus the shared date. This exercise blends self-joins with equality joins on dates—rules you’ll reuse when chaining sequential delivery waves in Prime Day or AWS migration schedules.

2. How would you list the duplicate rows in a users table?

   Group on the natural-key columns and add HAVING COUNT(*) > 1; join that result back to the base table to expose every duplicated record (including surrogate IDs) for cleanup. Amazon values this question because deduplication scripts are table stakes in Seller Central ETL pipelines.

3. How many customers were upsold—users who bought additional products on a later day than their first purchase?

   For each user calculate MIN(purchase_date) as first_date; count users whose MAX(purchase_date) > first_date. Same-day bundles don’t count. The problem checks date comparisons and distinct‐day logic—important for measuring the true incremental impact of “Frequently Bought Together” widgets.

4. Create a January-2020 histogram of comments per user (bin size = 1) and include a “0” bucket for users with no January comments.

   Build a numbers table (0…N) with generate_series, left-join aggregated January counts, and COALESCE missing values to zero. It tests whether you can manufacture absent buckets—vital for accurate funnel charts in Amazon Dashboards.

5. For each product and date, what is the running total of sales since launch?

   Use SUM(price) OVER (PARTITION BY product_id ORDER BY purchase_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) and sort by product_id, purchase_date. Window-function know-how is essential when you materialize incremental fact tables in Redshift.

6. Which two students have the closest SAT scores, and what is that difference?

   Sort students by score, compute LEAD(score) to get adjacent gaps, pick the lowest gap, and tie-break alphabetically on concatenated names. The task merges analytic functions with deterministic tie-breaks—skills used for “closest price” or “nearest seller” features.

7. How many bank customers made at least one transaction on each of the first five days of January 2020?

   Filter to the date range, count distinct days per user_id, and keep those with count = 5. This shows you grasp set completeness—critical for loyalty-streak and retention analyses.

8. What is each New York rider’s average commute time, and what is the city-wide NY average?

   Compute per-trip minutes, average by rider, then use a roll-up or sub-query to calculate the overall mean. Interviewers confirm you can derive two aggregation levels in one result—common in Last-Mile reporting.

9. What percentage of lifetime revenue came from the earliest year on record and from the latest year, rounded to two decimals?

   Sum revenue per year, isolate MIN(year) and MAX(year) with conditional sums, divide by total revenue, then round. It tests conditional aggregation plus percent math—handy for yearly GMV retrospectives.

10. Which three departments (≥ 10 employees) have the highest share of staff earning > $100 K? Rank them.

    Aggregate head-count and SUM(salary > 100000) per department, filter on head-count, compute the percentage, and order by that metric descending, keeping the top three. This blends post-aggregation filters with ratio calculations used in internal comp analytics.

11. Pivot exam scores so each student has columns for exams 1 – 4. How would you structure the query?

    Use conditional aggregates or FILTER clauses: MAX(score) FILTER (WHERE exam_id = 1) as exam_1, etc. The interviewer checks that you can reshape tall event logs into wide reporting tables.

12. What percentage of users achieved at least one seven-day visit streak?

    For each user, detect consecutive-day windows with LAG or date-diff logic, flag streaks, then divide streak users by total users and round. This examines advanced date arithmetic and cohort segmentation.

13. Produce a wide table showing total monthly sales for each product, one column per product.

    Combine SUM(price) with CASE WHEN product_id = X THEN price END pivots, grouped by month, or apply a PIVOT clause if supported. Demonstrates dynamic pivoting that Amazon BI teams need for glanceable dashboards.

14. How would you calculate month-over-month revenue change for 2019, rounded to two decimals?

    Sum monthly revenue, then apply LAG(total_revenue) to get the prior-month value and (curr - prev)/prev as MoM. Shows mastery of window functions for time-series analytics.

15. Within each department, list the top three salaries (or fewer if headcount is smaller), sorted by department asc, salary desc.

    Apply DENSE_RANK() OVER (PARTITION BY dept ORDER BY salary DESC) and filter rank ≤ 3. Maintaining order after filtering verifies you control both windowing and result presentation.

16. Which employees started before their manager? Return those employee names.

    Self-join employees e JOIN managers m ON e.manager_id = m.id, compare e.start_date < m.start_date, and project e.name. This confirms comfort with hierarchical joins.

17. Who are the five users with the longest continuous streak of daily visits?

    Use a gaps-and-islands technique: date - ROW_NUMBER() OVER (...) to form streak groups, count lengths, then ORDER BY streak DESC LIMIT 5. Amazon asks to ensure you can solve classic date-streak problems efficiently.

18. For 2020, show monthly user counts, transaction counts, and total order amount.

    Extract month, aggregate COUNT(DISTINCT user_id), COUNT(order_id), and SUM(order_amount); constrain to 2020 via WHERE YEAR(date) = 2020. Demonstrates multi-metric reporting in one scan.

19. After each restock, what is the running total of sales for every product?

    Join sales to the latest restock date per product using a max-date join, then compute cumulative SUM(price) with a window on product_id ordered by sale date. This question tests semi-complex window logic and event alignment.

20. Identify customers with > 3 transactions both in 2019 and 2020.

    Aggregate COUNT(*) by customer_id, YEAR(date), filter > 3, pivot or intersect years, and output qualifying IDs. Highlights multi-year cohort logic.

21. Do users order more to their primary address or to other addresses? Return the percentage of orders to home.

    Compare counts where ship_address_id = primary_address_id vs total orders and compute ROUND(home/total, 2). Shows conditional aggregation and ratio building.

22. Which user averages the highest number of unique item categories per order?

    Per order, count distinct categories; then average per user; finally ORDER BY avg_unique DESC LIMIT 1. Combines distinct counts, nested grouping, and ranking.

23. How would you sample every fourth transaction chronologically?

    Assign ROW_NUMBER() OVER (ORDER BY txn_date) and WHERE row_num % 4 = 0. Verifies window numbering and modular arithmetic—useful for down-sampling massive logs.

24. Which shipments were delivered during a customer’s membership period? Flag each as Y/N.

    Join shipments to membership spans and use CASE WHEN ship_date BETWEEN membership_start AND membership_end THEN 'Y' ELSE 'N' END. The question mixes range joins with categorical flag creation—common in Prime benefit audits.

Advanced SQL Interview Questions (5+ Yrs)

For Staff-level engineers, senior analysts, and data infrastructure leads, SQL technical interview questions become more open-ended and system-aware. These prompts test not just logic—but your ability to write production-grade SQL at scale.

Focus areas:

• Redshift Scalability – Working with DISTSTYLE, SORTKEY, and query slots
• Cross-System Logic – Handling joins between S3 + Redshift via Spectrum
• Query Mentoring – Explaining optimization decisions for education and review

Expect scenario-driven questions involving Amazon Redshift, data lake integration, and query tuning over partitioned fact tables. Some questions even come with follow-ups like “How would you teach this to a junior analyst?”—testing your ability to mentor and abstract your thinking.

1. How would you return the cumulative count of new users per day, resetting the running total at the start of each month?

   Extract the calendar date, MONTH(date) as month_key, and apply COUNT(*) OVER (PARTITION BY month_key ORDER BY date) to produce a daily running sum. Grouping isn’t needed; the window handles ordering and monthly reset logic. This question probes window‐function partitioning—critical for monthly active-user dashboards on dynamic retail data.

2. For every city-pair, which flight has the second-longest duration, and how would you list those flight IDs in ascending order?

   Normalize city pairs with LEAST(src,dst) and GREATEST(src,dst), then DENSE_RANK() duration descending inside each pair. Filter on rank = 2 and ORDER BY flight_id. Interviewers watch for city-pair canonicalization and correct handling of ties—skills used when Amazon Air analyzes route alternatives.

3. Does any user have overlapping completed subscription periods? Return true / false per user.

   Self-join on identical user_id where a.end_date >= b.start_date AND a.start_date < b.start_date, keeping only completed rows. Use EXISTS to flag overlap; coalesce NULL → false. This asks for interval-overlap logic—handy in AWS billing or Prime subscription audits.

4. How would you compute three-month retention for each monthly cohort and plan ID?

   Derive start_month as DATE_TRUNC('month', start_date), assign cohorts, then pivot three boolean “retained” flags using CASE logic on month offsets. Roll up with AVG() to get retention percentages per cohort-month and sort start_month, plan_id, num_month. It tests multi-step cohort math Amazon uses to track Prime conversion.

5. How much time (in whole minutes) did each plane spend in the air each day?

   For every flight row compute TIMESTAMPDIFF(MINUTE, departure_ts, arrival_ts), then sum by plane_id, flight_date (cast from departure). Round down via integer math. Interviewers confirm you can combine date extraction and aggregation for operational reports.

6. Which five product pairs are most frequently purchased together, and how would you list their names alphabetically within each pair?

   Self-join transactions on identical user_id with product_id1 < product_id2 to avoid mirror duplicates, group by the two product IDs, count occurrences, order by COUNT(*) DESC LIMIT 5, then join to the products lookup table for names. The prompt gauges your ability to handle billion-row self-joins efficiently—a real-world need in Amazon’s recommendation engine.

7. Write a query that returns a three-day rolling average of deposit amounts for each calendar day, formatted ‘YYYY-MM-DD’.

   Filter value > 0, cast created_at to date, sum deposits per day, then apply AVG(sum_value) OVER (ORDER BY date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW). Format the date with TO_CHAR(date,'YYYY-MM-DD'). Tests multi-step aggregation plus rolling-window syntax.

8. How would you create a flight_routes table containing each unique city pair only once, regardless of direction?

   Select LEAST(src,dst) AS city_a, GREATEST(src,dst) AS city_b from flights, group by the two columns to deduplicate, and INSERT into the new table. Highlights canonicalization patterns used in logistics networks where Dallas → Seattle equals Seattle → Dallas.

Not all Amazon SQL interviews run on the same database backend. Depending on your team—whether AWS, Retail, Alexa, or Advertising—you may be asked to write or optimize SQL queries across different platforms like Redshift, MySQL, PostgreSQL, or SQL Server. Understanding the quirks of each system can help you avoid syntax errors, write more performant code, and demonstrate production-level thinking.

Amazon Redshift

Redshift is Amazon’s go-to data warehouse, used heavily across Retail and AWS teams. Interview questions here often center around distribution strategies, query optimization, and massive-scale data handling. You might be asked how to choose the right DISTKEY and SORTKEY for a 3TB table, or how to improve join performance when pulling from multiple fact and dimension tables. Make sure you understand concepts like columnar storage, spectrum queries, and concurrency scaling.

MySQL

Some Amazon teams, especially those closer to transactional systems or legacy products, still use MySQL. Here, questions test your familiarity with syntax differences (LIMIT vs TOP), string handling, and index use. Be ready to adapt a query from one dialect to MySQL, and handle edge-cases like NULL sorting or how GROUP BY treats non-aggregated fields in different SQL modes.

PostgreSQL

PostgreSQL appears in backend engineering and product analytics roles at Amazon. Expect interview questions around window functions, date/time logic, and sometimes user-defined functions (UDFs). Knowing the PostgreSQL-specific nuances—like using FILTER within aggregates or the availability of GENERATE_SERIES()—can help you write more expressive and elegant queries during interviews.

SQL Server

Though less common, SQL Server shows up in interviews for finance, advertising, and some AWS ops teams. Interviewers may probe your understanding of data type conversions (TRY_CONVERT() vs CAST()), temporary tables, or T-SQL stored procedures. Pay attention to how error handling works differently here compared to open-source databases, and be ready to deal with verbose syntax and quirks like CTE evaluation order.

Getting the SQL question technically right is one thing. Solving it in a scalable, production-ready way is what sets apart top-tier Amazon candidates.

Getting the SQL question technically right is one thing. Solving it in a scalable, production-ready way is what sets apart top-tier Amazon candidates.

Below are some of the most searched questions about Amazon SQL interviews, designed to help you prep faster and smarter.

How often are Amazon SQL questions asked in interviews?

Amazon SQL interview questions show up in nearly every data analyst, BI engineer, and product analyst round—especially in the first technical screen.

Are Amazon SQL interviews timed?

Yes. Many roles start with a timed SQL assessment test (often 60–90 minutes), followed by live sessions for query walkthroughs.

What SQL dialect does Amazon use?

It depends. Amazon uses a mix of Redshift (PostgreSQL-like), MySQL, and proprietary AWS tools. Always ask the recruiter what to prep for.

Can I use AI tools during the take-home?

No. Amazon disallows outside assistance during take-home challenges and can flag plagiarism, especially in SQL coding test platforms.

What if I’m rusty on SQL?

Focus on the basics: joins, aggregates, and filtering. Then build up to nested queries and window functions using mock assessments.

Succeeding in Amazon’s technical screens isn’t just about solving SQL interview questions—it’s about how you think through edge cases, explain trade-offs, and write scalable code. Strong candidates don’t just memorize syntax; they demonstrate fluency with joins, aggregates, filtering, and indexing while narrating their reasoning clearly.

The best prep involves repetition under realistic pressure: simulate the experience using Interview Query’s AI SQL Interviewer or schedule a live session via our Mock Interviews platform. You can also follow our structured SQL Learning Path to build fluency from the ground up. If you need a confidence boost, check out how Simran Singh landed her dream data role after applying to 4,200 jobs—with SQL as her make-or-break skill.