Amazon·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

SQL-heavy technical screen for a software engineering role at Amazon. Just one question but it had multiple layers and they kept pushing to see how far I'd take it.

Questions Asked (1)

You have a products table with a department/category/subcategory hierarchy and a click_log table with user and event data. Write a query to count distinct users who clicked products in a given department within a time range. Then extend it to return counts for all departments at once.

Data ModelingTechnical Trade-offs

Author's notes

Started with the filtered version which was fine, just a join plus COUNT(DISTINCT user_id) with a WHERE on department and event_time.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the schema and requirements, then write a straightforward query joining click_log to products on product_id, filtering by department and time range, and counting distinct user_id. For the extension, use GROUP BY department to get counts for all departments in one query, ensuring the join and filters are applied correctly.

Pro tip: Mention that you would verify the grain of the click_log table and consider whether clicks are recorded at the product level or event level, as this affects the join and distinct count. Also, discuss the trade-off between a single grouped query and multiple queries in terms of performance and readability.

1. Clarify requirements and schema

Ask about the table structures, especially the hierarchy columns (department, category, subcategory) and the click_log columns (user_id, product_id, timestamp). Confirm the time range boundaries and whether 'clicked' means any event or a specific event type.

2. Write the base query for a single department

Join click_log to products on product_id, filter by department and timestamp range, then count distinct user_id. Use appropriate date functions and ensure the join is efficient.

3. Extend to all departments

Remove the department filter and add GROUP BY department to the query, selecting department and COUNT(DISTINCT user_id). This returns counts for all departments in one result set.

4. Optimize and discuss trade-offs

Consider indexing on product_id and timestamp, and whether to pre-aggregate or use a subquery. Discuss the trade-off between a single grouped query (efficient but may scan more data) versus multiple queries (simpler but more round trips).

Key Points to Mention

Use COUNT(DISTINCT user_id) to avoid double-counting users who clicked multiple products.
Join click_log to products on product_id, and filter by department and timestamp range.
For all departments, use GROUP BY department instead of running separate queries.
Consider the grain of click_log: if a user can have multiple events per product, ensure distinct count is on user_id, not event_id.
Discuss indexing strategies on product_id, department, and timestamp to improve performance.
Mention the trade-off between a single grouped query and multiple queries in terms of performance and maintainability.

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