Window function: running total over category

Last updated: January 19, 2026

Quick Overview

Use window functions to compute rank partitioned by date.

Uber

Data Manipulation (SQL/Python)

Data Scientist

Uber

January 19, 2026

Data Scientist

Take-home Project

Data Manipulation (SQL/Python)

Medium

804 solved

Use window functions to compute rank partitioned by date.

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

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

Subqueries and correlated subqueries

Common Table Expressions (CTEs)

JOIN types and when to use each

Aggregate functions and GROUP BY

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 optimize this query for a table with 100 million rows?
How would you validate the correctness of your query results?
What would you do if this query needs to run every 5 minutes?

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Practice SQL Problems

Sample Answer

Problem Understanding

To address the problem, we need a dataset that includes at least two key fields: date and category. Our objective is to compute a running total of counts (or sums) partitioned by date and ordere...

Approach

Identify the Dataset: Start with a table (e.g., transactions) that includes at least the date, category, and a field for aggregation, such as amount or count.
**Common Table Expres...

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