Calculate percentile rank per date

Last updated: January 8, 2026

Quick Overview

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

Snowflake
Data Manipulation (SQL/Python)
Data Scientist
Snowflake
January 8, 2026
Data Scientist
Onsite
Data Manipulation (SQL/Python)
Medium

1

4

2,351 solved


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

Snowflake 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
Data cleaning and transformation
Index optimization and query performance
Common Table Expressions (CTEs)
Subqueries and correlated subqueries
NULL handling and COALESCE
Window functions (ROW_NUMBER, RANK, LAG, LEAD)
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
  • What indexes would you create to support this query?
  • 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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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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