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Chime·Data Scientist·Technical Phone Screen·Senior

Senior
Jun 2026

Summary

Chime data scientist interview with a single heavy SQL question that covered basically every window function you've ever heard of. The kind of problem where you finish writing it and still aren't sure if it's right.

Questions Asked (1)

Q1

Given a users table and a transactions table, write a single SQL query using CTEs that returns, per user per calendar week: weekly revenue, a 4-week rolling sum of revenue, week-over-week percent change, and a dense rank of users by rolling revenue within each week. Also return each user's first purchase date and the number of days between their first and second purchase. Filter to rows where weekly revenue dropped at least 20% versus the prior week, and only show the top 3 ranks per week. Users with fewer than 2 purchases should still appear with a NULL for days to second purchase.

Data ModelingProduct Analytics & MetricsAlgorithms & Data Structures
Author's notes

This thing had like five requirements stapled together and I kept losing track of which CTE was supposed to feed which.

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AI HintsAI Generated

Suggested Approach

Break the problem into modular CTEs: first aggregate weekly revenue per user, then compute rolling sums, week-over-week changes, and ranks using window functions. Separately calculate first and second purchase dates per user, then join and apply filters for revenue drop and top ranks.

Pro tip: Always clarify the definition of 'calendar week' (e.g., week starting Monday vs. Sunday) and how to handle users with no prior week (e.g., NULL for week-over-week change) to avoid off-by-one errors.

1. Aggregate weekly revenue per user

Create a CTE that groups transactions by user and calendar week, summing revenue. Ensure you handle weeks with zero revenue if needed, but typically only weeks with purchases are included.

2. Compute window functions for rolling metrics

Using the weekly revenue CTE, calculate a 4-week rolling sum, week-over-week percent change (using LAG), and a dense rank of users by rolling revenue within each week.

3. Calculate first and second purchase dates

Create a separate CTE that finds each user's first and second purchase dates using ROW_NUMBER or MIN and filtering. Compute days between them, leaving NULL if no second purchase.

4. Join and filter results

Join the weekly metrics with the purchase date info, then filter to rows where weekly revenue dropped at least 20% versus the prior week and where the dense rank is <= 3.

Key Points to Mention

  • Use of CTEs to break down complex logic and improve readability.
  • Window functions: SUM() OVER for rolling sum, LAG() for week-over-week change, DENSE_RANK() for ranking.
  • Handling of calendar weeks: use DATE_TRUNC('week', transaction_date) or equivalent, and be mindful of week boundaries.
  • Dealing with NULLs: for week-over-week change when no prior week, and for days to second purchase when user has <2 purchases.
  • Filtering logic: revenue drop condition (current week revenue <= 0.8 * prior week revenue) and top 3 ranks per week.
  • Performance considerations: indexing on user_id and transaction_date, and avoiding unnecessary cross joins.

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