Window function: rank over category
Last updated: March 5, 2026
Quick Overview
Use window functions to compute rank partitioned by date.
DE Shaw
Data Manipulation (SQL/Python)
Data Scientist
DE Shaw
March 5, 2026Data Scientist
Onsite
Data Manipulation (SQL/Python)
Hard
25
5
3,579 solved
Use window functions to compute rank partitioned by date.
DE Shaw 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
- 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
Common Table Expressions (CTEs)
Aggregate functions and GROUP BY
Index optimization and query performance
Window functions (ROW_NUMBER, RANK, LAG, LEAD)
Date/time manipulation
NULL handling and COALESCE
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
- How would you optimize this query for a table with 100 million rows?
- What indexes would you create to support this query?
- How would you handle this if the data was spread across multiple databases?
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Practice SQL ProblemsSample Answer
Problem Understanding
The task is to compute the rank of records partitioned by date and categorized by a specific attribute (e.g., 'category'). The dataset likely includes columns such as date, category, and a value...
Approach
- Identify the Dataset: Determine the relevant columns we need from the dataset, focusing on
date,category, and the ranking criterion (e.g.,value). - **Use Common Table Expressions (CTEs...
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