Calculate percentile rank per date

Last updated: September 10, 2025

Quick Overview

Write a query to compute percentile rank grouped by user, handling edge cases like nulls and duplicates.

Snapchat
Data Manipulation (SQL/Python)
Data Scientist
Snapchat
September 10, 2025
Data Scientist
Onsite
Data Manipulation (SQL/Python)
Medium

587

7

1,451 solved


Write a query to compute percentile rank grouped by user, handling edge cases like nulls and duplicates.

Snapchat asks this during the Onsite 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
  • 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
Common Table Expressions (CTEs)
Subqueries and correlated subqueries
NULL handling and COALESCE
JOIN types and when to use each
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
  • How would you handle slowly changing dimensions in this scenario?
  • How would you optimize this query for a table with 100 million rows?
  • How would you validate the correctness of your query results?
  • Can you rewrite this without using subqueries?
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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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