Window function: lead/lag over category

Last updated: January 17, 2026

Quick Overview

Use window functions to compute running total partitioned by date.

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

NULL handling and COALESCE

Window functions (ROW_NUMBER, RANK, LAG, LEAD)

JOIN types and when to use each

Date/time manipulation

Data cleaning and transformation

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?
Can you rewrite this without using subqueries?
How would you handle this if the data was spread across multiple databases?

Sharpen Your Skills on Codemia

Practice similar problems with our interactive workspace, get AI feedback, and track your progress.

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 ...

Submit Your Answer

Markdown supported

Meta Data Scientist Interview Guide

Interview process, tips, and preparation timeline