Window function: lead/lag over date

Last updated: April 26, 2026

Quick Overview

Use window functions to compute rank partitioned by date.

Apple

Data Manipulation (SQL/Python)

Data Scientist

Apple

April 26, 2026

Data Scientist

Phone Screen

Data Manipulation (SQL/Python)

Easy

280

2,144 solved

Use window functions to compute rank partitioned by date.

Apple asks this during the Phone Screen because data engineering skills are critical for the role. You should be comfortable with complex joins, window functions, CTEs, and performance optimization.

What the Interviewer Expects

Write syntactically correct SQL with proper JOIN and WHERE clauses
Use GROUP BY and aggregate functions appropriately
Handle NULL values correctly in your queries
Explain the query execution plan at a high level

Key Topics to Cover

JOIN types and when to use each

Subqueries and correlated subqueries

NULL handling and COALESCE

Window functions (ROW_NUMBER, RANK, LAG, LEAD)

Index optimization and query performance

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 this if the data was spread across multiple databases?
How would you validate the correctness of your query results?
What indexes would you create to support this query?

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Practice SQL Problems

Sample Answer

Problem Understanding

In this problem, we are tasked with using window functions to compute ranks partitioned by date. The data involved likely includes a table with at least two columns: a 'date' column and a 'value' colu...

Approach

Identify the Table: Assume the table is named sales_data with columns sale_date and sale_amount.
Partitioning: Use the PARTITION BY clause to group by sale_date so that ranks c...

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