Window function: lead/lag over category

Last updated: December 16, 2025

Quick Overview

Use window functions to compute rank partitioned by user_id.

Dropbox

Data Manipulation (SQL/Python)

Data Scientist

Dropbox

December 16, 2025

Data Scientist

Technical Screen

Data Manipulation (SQL/Python)

Medium

1,301 solved

Use window functions to compute rank partitioned by user_id.

This question from Dropbox's Technical 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

Use advanced SQL features: window functions, CTEs, subqueries
Write efficient queries that avoid common performance pitfalls
Handle complex data transformations with multiple joins and aggregations
Discuss indexing strategy and query optimization
Address data quality issues: duplicates, missing values, outliers

Key Topics to Cover

JOIN types and when to use each

NULL handling and COALESCE

Data cleaning and transformation

Window functions (ROW_NUMBER, RANK, LAG, LEAD)

Aggregate functions and GROUP BY

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

How would you optimize this query for a table with 100 million rows?
How would you validate the correctness of your query results?
Can you rewrite this without using subqueries?

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Sample Answer

Problem Understanding

In this problem, we are tasked with using window functions to compute the rank of records partitioned by user_id. The data involved likely includes user activity logs or transactions, with fields su...

Approach

Identify the Data: Ensure we have access to the table containing the relevant fields: user_id, category, and the criteria for ranking (e.g., transaction_value or timestamp).
**CTE ...

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