Optimize a slow query on users

Last updated: February 21, 2026

Quick Overview

A query on users is running slowly. Identify the bottleneck and optimize it.

Jane Street
Data Manipulation (SQL/Python)
Data Scientist
Jane Street
February 21, 2026
Data Scientist
Onsite
Data Manipulation (SQL/Python)
Hard

19

7

3,355 solved


A query on users is running slowly. Identify the bottleneck and optimize it.

Data manipulation questions at Jane Street test your ability to work with real-world datasets. This Onsite question evaluates your SQL proficiency, understanding of data modeling, and ability to derive insights from raw data.

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
Data cleaning and transformation
Window functions (ROW_NUMBER, RANK, LAG, LEAD)
Index optimization and query performance
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 validate the correctness of your query results?
  • What indexes would you create to support this query?
  • How would you handle slowly changing dimensions in this scenario?
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