Calculate average revenue per region

Last updated: March 16, 2026

Quick Overview

Write a query to compute average revenue grouped by user, handling edge cases like nulls and duplicates.

Uber
Data Manipulation (SQL/Python)
Data Scientist
Uber
March 16, 2026
Data Scientist
Phone Screen
Data Manipulation (SQL/Python)
Hard

37

7

4,967 solved


Write a query to compute average revenue grouped by user, handling edge cases like nulls and duplicates.

Uber asks this during the Phone Screen 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
  • Solve complex analytical problems with elegant, readable SQL
  • Optimize queries for large-scale datasets with partitioning and indexing
  • Use recursive CTEs, lateral joins, and advanced window functions
  • Design the data model alongside the query solution
  • Discuss trade-offs between SQL and programmatic approaches (Python/pandas)
  • Consider the operational aspects: query scheduling, incremental processing
Key Topics to Cover
Aggregate functions and GROUP BY
NULL handling and COALESCE
Window functions (ROW_NUMBER, RANK, LAG, LEAD)
Subqueries and correlated subqueries
Common Table Expressions (CTEs)
Date/time manipulation
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
  • Can you rewrite this without using subqueries?
  • How would you validate the correctness of your query results?
  • What indexes would you create to support this query?
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