Join messages and impressions to find retention rate
Last updated: April 26, 2026
Quick Overview
Write a query joining messages and rides to produce the combined retention rate.
DE Shaw
Data Manipulation (SQL/Python)
Data Scientist
DE Shaw
April 26, 2026Data Scientist
Phone Screen
Data Manipulation (SQL/Python)
Hard
37
8
3,024 solved
Write a query joining messages and rides to produce the combined retention rate.
This question from DE Shaw's Phone Screen tests practical data skills. The interviewer wants to see clean, efficient queries that handle edge cases like NULLs, duplicates, and large datasets.
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
Pandas vectorized operations and groupby
Window functions (ROW_NUMBER, RANK, LAG, LEAD)
Index optimization and query performance
JOIN types and when to use each
Common Table Expressions (CTEs)
How to Approach This
- Clarify the schema and expected output format before writing queries.
- Use CTEs (WITH clauses) to break complex queries into readable steps.
- Consider window functions (ROW_NUMBER, RANK, LAG, LEAD) for ranking and sequential analysis.
- Watch for NULLs, duplicates, and edge cases in JOINs and GROUP BY.
- For pandas, prefer vectorized operations over row-by-row iteration.
Possible Follow-up Questions
- What indexes would you create to support this query?
- How would you handle slowly changing dimensions in this scenario?
- Can you rewrite this without using subqueries?
- How would you validate the correctness of your query results?
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Practice SQL ProblemsSample Answer
Problem Understanding
To compute the retention rate, we need to join two datasets: messages and rides. The messages dataset contains records of user interactions (like sending or receiving messages), while the `rides...
Approach
- Identify relevant columns: From
messages, we need user IDs and message timestamps. Fromrides, we need user IDs and ride timestamps. - Join the datasets: Use an INNER JOIN on user IDs ...
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