Query Construction | SQL for Product Managers

SQL for Product Managers

The Query Is the New Follow-Up Question

How a Database Is Actually Organized

Reading Your First Query Without Fear

Where PMs Actually Sit Between Dashboards and Raw Data

The Query Lifecycle, From Question to Decision

SELECT, FROM, WHERE: Asking a Precise Question

Filtering and Sorting: WHERE, ORDER BY, LIMIT

Aggregating Data: GROUP BY, COUNT, SUM, AVG

JOINs: Connecting Tables Without Breaking Your Numbers

Common Mistakes That Quietly Give You the Wrong Answer

Funnel Analysis in SQL

Cohort and Retention Queries

Segmenting Users: CASE WHEN and Subqueries

Time-Based Analysis: Dates, Trends, and Comparisons

Sanity-Checking Your Own Query

From Query to Decision: Framing the Ask

Writing a Data Request an Analyst Can Actually Use

When to Write SQL Yourself vs. When to Ask for Help

Window Functions, Explained Without Fear

CTEs: Making Complex Queries Readable

Speaking the Language of Data Teams

SQL and the Modern Data Stack, for PMs

Capstone: Write and Run a Real Product Metrics Query Set

The Four Required Deliverables

How You Will Be Assessed

Foundations and Technical Fluency

Product Judgment

Query Construction

Data Quality and Trust

Scenario and Case Style

Lesson 4

Query Construction

11. Write a query to find the top 5 customers by total revenue.

Tests GROUP BY, SUM, ORDER BY, and LIMIT together, and whether the candidate remembers to sort before limiting.

12. Write a query to find users who signed up but never placed an order.

Tests LEFT JOIN combined with a WHERE ... IS NULL filter on the right-hand table, a very common real-world pattern.

13. How would you calculate month-over-month percentage growth in revenue using SQL?

Tests awareness of window functions (LAG) or self-joins, and the arithmetic of a percentage-change calculation.

14. What's a CTE, and why would you use one instead of a nested subquery?

Look for readability and maintainability as the answer, not just "it's a different syntax."

15. Explain what a window function does differently from GROUP BY.

A strong answer explains that window functions preserve individual rows while still performing the aggregate-style calculation across a defined set of related rows.