Calculate percentile rank per user

Last updated: December 14, 2025

Quick Overview

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

Atlassian

Data Manipulation (SQL/Python)

Data Scientist

Atlassian

December 14, 2025

Data Scientist

Technical Screen

Data Manipulation (SQL/Python)

Medium

3,932 solved

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

This question from Atlassian'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

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)

Window functions (ROW_NUMBER, RANK, LAG, LEAD)

Date/time manipulation

JOIN types and when to use each

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 optimize this query for a table with 100 million rows?
Can you rewrite this without using subqueries?
How would you validate the correctness of your query results?

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Practice SQL Problems

Sample Answer

Problem Understanding

The task is to compute the percentile rank of some metric (e.g., scores, sales, etc.) grouped by users. This means we will need a table that contains user identifiers and the corresponding values for ...

Approach

To build the query, we can follow these steps:

Identify the relevant tables: Assume we have a table named user_metrics with columns user_id and metric_value.
Filter out NULL values...

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