Window function: lead/lag over category
Last updated: November 15, 2025
Quick Overview
Use window functions to compute rank partitioned by user_id.
Robinhood
Data Manipulation (SQL/Python)
Data Scientist
Robinhood
November 15, 2025Data Scientist
Onsite
Data Manipulation (SQL/Python)
Hard
9
8
3,503 solved
Use window functions to compute rank partitioned by user_id.
This question from Robinhood's Onsite 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
JOIN types and when to use each
Aggregate functions and GROUP BY
Data cleaning and transformation
Index optimization and query performance
Date/time manipulation
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 handle slowly changing dimensions in this scenario?
- How would you optimize this query for a table with 100 million rows?
- What would you do if this query needs to run every 5 minutes?
- Can you rewrite this without using subqueries?
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Problem Understanding
In this problem, we are tasked with analyzing user transaction data to compute a rank for each transaction, partitioned by user_id. The relevant data involves transaction records that may include fi...
Approach
- Select Data: Start by selecting relevant columns such as
user_id,transaction_date, andamountfrom the transactions table. - Handle NULLs: Ensure to consider how NULL values may a...
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