Calculate rolling 7-day average per date

Last updated: January 25, 2026

Quick Overview

Write a query to compute rolling 7-day average grouped by date, handling edge cases like nulls and duplicates.

Discord
Data Manipulation (SQL/Python)
Data Scientist
Discord
January 25, 2026
Data Scientist
Technical Screen
Data Manipulation (SQL/Python)
Easy

20

3

3,654 solved


Write a query to compute rolling 7-day average grouped by date, handling edge cases like nulls and duplicates.

This question from Discord'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
  • 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
Pandas vectorized operations and groupby
Common Table Expressions (CTEs)
Aggregate functions and GROUP BY
Data cleaning and transformation
Index optimization and query performance
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
  • Can you rewrite this without using subqueries?
  • How would you handle slowly changing dimensions in this scenario?
  • How would you handle this if the data was spread across multiple databases?
  • What would you do if this query needs to run every 5 minutes?
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Practice SQL Problems
Sample Answer
Problem Understanding

We need to compute a rolling 7-day average of a specific metric (let's assume it's 'user_count') grouped by date. The data is assumed to be stored in a table called daily_users with at least two col...

Approach
  1. Data Preparation: Ensure that there are no NULL values in the user_count column by replacing them with 0 (or another appropriate value).
    2. Aggregation: Use a window function to calc...

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