Calculate percentile rank per date

Last updated: March 12, 2026

Quick Overview

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

Goldman Sachs
Data Manipulation (SQL/Python)
Data Scientist
Goldman Sachs
March 12, 2026
Data Scientist
Technical Screen
Data Manipulation (SQL/Python)
Medium

10

0

4,755 solved


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

Goldman Sachs asks this during the Technical 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
  • 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
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
  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 validate the correctness of your query results?
  • How would you optimize this query for a table with 100 million rows?
  • How would you handle slowly changing dimensions in this scenario?
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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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