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Salary Percentile Ranking
Meta's comp team wants to understand where each employee falls in the company-wide salary distribution — useful for refresh cycle calibration and benchmarking against external offers. Show name, department, salary, and salary_percentile (using PERCENT_RANK, rounded to 2 decimals). Sort by salary_percentile descending. PERCENT_RANK is a Meta interview classic because relative ranking (how do you stack up vs peers?) is a different mental model than absolute ranking (what's your rank number?), and Meta's bar is catching whether you know that difference cold.
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Schema
employees
| emp_id | name | department | position | salary | hire_date | manager_id | performance_rating |
|---|---|---|---|---|---|---|---|
| 1 | Alice Johnson | Engineering | Senior Developer | 95000 | 2019-03-15 | 5 | 4.5 |
| 2 | Bob Smith | Engineering | Developer | 75000 | 2020-06-01 | 1 | 3.8 |
| 3 | Carol Williams | Marketing | Marketing Manager | 85000 | 2018-09-20 | NULL | 4.2 |
Expected output: 95k → 1.0, 72k → 0.67, 55k → 0.33, 48k → 0.0
Hint
Concepts
SELECT Window Functions PERCENT_RANK ROUND
Practise the topic: SQL practice questions · Window function practice · Ranking function practice · Advanced SQL interview questions
In these company practice sets
Google · Meta · NVIDIA · Snowflake
A SQL Quest challenge matched to patterns reported for these companies — not a question any of them has published.
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