Calculate rolling 7-day average per region

Last updated: February 6, 2026

Quick Overview

Write a query to compute rolling 7-day average grouped by user, handling edge cases like nulls and duplicates.

Cloudflare
Data Manipulation (SQL/Python)
Data Scientist
Cloudflare
February 6, 2026
Data Scientist
Onsite
Data Manipulation (SQL/Python)
Medium

38

0

4,201 solved


Write a query to compute rolling 7-day average grouped by user, handling edge cases like nulls and duplicates.

Cloudflare asks this during the Onsite because data engineering skills are critical for the role. You should be comfortable with complex joins, window functions, CTEs, and performance optimization.

What the Interviewer Expects
  • Use advanced SQL features: window functions, CTEs, subqueries
  • Write efficient queries that avoid common performance pitfalls
  • Handle complex data transformations with multiple joins and aggregations
  • Discuss indexing strategy and query optimization
  • Address data quality issues: duplicates, missing values, outliers
Key Topics to Cover
Pandas vectorized operations and groupby
Window functions (ROW_NUMBER, RANK, LAG, LEAD)
Subqueries and correlated subqueries
Common Table Expressions (CTEs)
How to Approach This
  1. Clarify the schema and expected output format before writing queries.
  2. Use CTEs (WITH clauses) to break complex queries into readable steps.
  3. Consider window functions (ROW_NUMBER, RANK, LAG, LEAD) for ranking and sequential analysis.
  4. Watch for NULLs, duplicates, and edge cases in JOINs and GROUP BY.
  5. For pandas, prefer vectorized operations over row-by-row iteration.
Possible Follow-up Questions
  • How would you optimize this query for a table with 100 million rows?
  • Can you rewrite this without using subqueries?
  • How would you validate the correctness of your query results?
  • How would you handle slowly changing dimensions in this scenario?
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