Amazon·Data Scientist·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Amazon Data Scientist interview with a SQL-heavy technical screen. The problem was framed around an e-commerce dashboard scenario and required both aggregation logic and window functions back to back.

Questions Asked (2)

Given a Customers table and a Sales table, write a SQL query that joins them and returns total revenue grouped by region.

Data ModelingProduct Analytics & Metrics

Author's notes

Pretty standard join-and-aggregate question.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the schema and business context, then write a SQL query that joins Customers and Sales on customer_id, groups by region, and sums revenue. Explain your assumptions and consider edge cases like NULLs or duplicate customers.

Pro tip: Mention that you would validate the join cardinality and check for duplicate customer records to avoid revenue inflation, and discuss how you'd handle customers with no sales if the business needs them included.

1. Clarify schema and requirements

Ask about table structures, join keys, and whether 'total revenue' means sum of sales amount or quantity*price. Confirm if regions come from Customers and if customers without sales should be included.

2. Choose the right join type

Use INNER JOIN if only customers with sales matter; LEFT JOIN if all customers should appear (with 0 revenue). Explain the trade-off.

3. Write the SQL query

Select region, SUM(revenue) as total_revenue, FROM Customers JOIN Sales ON Customers.customer_id = Sales.customer_id, GROUP BY region. Use COALESCE for NULL handling if needed.

4. Validate and optimize

Check for duplicate customer_id in Customers (which would multiply sales) and consider indexing join keys. Mention that you'd test with sample data.

5. Discuss extensions

Talk about adding filters (e.g., date range), handling NULL regions, or using window functions for additional metrics like revenue per customer.

Key Points to Mention

Join key and cardinality (one-to-many between Customers and Sales)
INNER vs LEFT JOIN and business implications
GROUP BY region with SUM aggregation
Handling NULLs in region or revenue
Data quality checks (duplicates, missing values)
Performance considerations (indexes, partitioning)

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

Extend the previous query using window functions to rank customers by total spending within each region and return only the top 3 per region.

Data ModelingProduct Analytics & MetricsAlgorithms & Data Structures

Author's notes

This is where I slowed down.

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AI HintsAI Generated

Suggested Approach

Start by explaining the need for a subquery or CTE to compute total spending per customer per region, then apply a window function like RANK() or DENSE_RANK() partitioned by region and ordered by total spending descending. Finally, filter the results to keep only rows where the rank is <= 3, ensuring ties are handled appropriately.

Pro tip: Clarify whether to use RANK() or DENSE_RANK() based on how ties should affect the top 3; in many business contexts, DENSE_RANK() is preferred to avoid skipping ranks, but confirm with the interviewer. Also, mention that window functions are computed after WHERE but before ORDER BY, so filtering on the rank requires a subquery or CTE.

1. Aggregate total spending per customer per region

Use a GROUP BY on customer and region to sum the spending, creating a base result set with total_spend for each customer-region pair.

2. Apply window function to rank customers within each region

In a subquery or CTE, use RANK() or DENSE_RANK() OVER (PARTITION BY region ORDER BY total_spend DESC) to assign a rank to each customer within their region.

3. Filter to top 3 per region

In the outer query, filter the ranked results to include only rows where the rank is <= 3, ensuring you get the top 3 customers per region.

4. Handle ties and validate results

Decide on the ranking function based on tie-handling requirements, and optionally validate by checking counts per region or comparing with a manual calculation.

Key Points to Mention

Use of PARTITION BY region to reset ranking for each region.
Choice between RANK(), DENSE_RANK(), and ROW_NUMBER() and their implications for ties.
Need for a subquery or CTE because window functions cannot be used in WHERE clause directly.
Aggregation step to compute total spending per customer per region before ranking.
Performance considerations: indexing on region and customer, and potential use of QUALIFY in some databases (e.g., Snowflake, BigQuery).
Edge cases: regions with fewer than 3 customers, ties at the boundary, and null values.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.