Window function: running total over category

Last updated: December 5, 2025

Quick Overview

Use window functions to compute running total partitioned by date.

Shopify
Data Manipulation (SQL/Python)
Data Scientist
Shopify
December 5, 2025
Data Scientist
Technical Screen
Data Manipulation (SQL/Python)
Medium

10

7

3,897 solved


Use window functions to compute running total partitioned by date.

Data manipulation questions at Shopify test your ability to work with real-world datasets. This Technical Screen 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
Data cleaning and transformation
Pandas vectorized operations and groupby
Subqueries and correlated subqueries
Date/time manipulation
JOIN types and when to use each
NULL handling and COALESCE
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 handle this if the data was spread across multiple databases?
  • What indexes would you create to support this query?
  • Can you rewrite this without using subqueries?
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Practice SQL Problems
Sample Answer
Approach

Break the problem into logical steps before writing SQL. Think about: 1. What tables do I need to join and on which keys? 2. What filtering (WHERE) d...

Solution Pattern

```sql WITH filtered_data AS ( SELECT * FROM main_table WHERE condition = 'value' AND date_col >= '2024-01-01' ), aggregated AS ( SELECT ...


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