ROW_NUMBER: Find the Highest-Paid Employee in Each Department

SQL coding challenge · Difficulty: easy · Topic: Window Functions · +50 XP

Problem

Each department head wants to see their top earner in the team dashboard. Find the single highest-paid employee in each department.

Tables

Table: employees_wf

| emp_id | emp_name | department | salary |
| 101 | John | IT | 90000 |
| 102 | Alice | IT | 75000 |
| 103 | Bob | IT | 75000 |
| 104 | David | HR | 80000 |
| 105 | Emma | HR | 65000 |

Expected Output

| emp_id | emp_name | department | salary |
| 104 | David | HR | 80000 |
| 101 | John | IT | 90000 |

• Return: emp_id, emp_name, department, salary
• One row per department — the highest paid employee
• Sort by department ascending
• Hint: Use ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC) in a subquery, then filter where rn = 1

What this SQL challenge teaches you

“ROW_NUMBER: Find the Highest-Paid Employee in Each Department” is a easy-level SQL challenge focused on Window Functions. Working through it gives you hands-on practice with ROW_NUMBER, PARTITION BY, Subquery — the kind of transformation you are asked to write in real data engineering work and in technical interviews. You can solve it directly in the browser: the dataset is pre-loaded, so you write the query or DataFrame code, run it, and compare your output against the expected result immediately.

Concepts covered

• ROW_NUMBER
• PARTITION BY
• Subquery

How to approach it

If you get stuck, work through these steps in order before looking at a full solution — each one narrows the problem down:

1. Your result should return: emp_id, emp_name, department, salary.
2. Order the output department ascending.
3. Use ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC) in a subquery, then filter where rn = 1
4. Compare your output to the Expected Output — the columns, values and row order must match exactly.

Where this comes up

Variations of this problem have been reported in interviews at Amazon, Microsoft, Infosys, TCS. Interviewers use it to check whether you can express the logic cleanly and reason about correctness on edge cases such as ties, nulls and empty groups.

How to practise it on PySpark.in

Open the challenge, write your SQL query in the editor and press Run to execute it against the sample dataset. Submitting checks your output against every test case, including hidden ones, so you find out straight away whether your logic holds up. You can retry as often as you like, and each solved challenge adds to your XP.

Related SQL challenges

• Top 3 Products per Category
• Running Total Revenue
• Median Salary per Department
• Latest Order Per Customer
• 3-Day Rolling Sum of Sales
• 7-Day Rolling Purchase Amount by Customer

Frequently asked questions

Do I need to install Spark or a database to solve this?

No. The SQL environment runs in your browser with the sample data already loaded, so there is nothing to install or configure.

Is this challenge free?

Yes - the problem, the sample dataset, the hints and unlimited test runs are free.

What level is it?

It is rated easy and covers Window Functions.

Solve this challenge free on PySpark.in