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 Brief

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 5

Data Quality and Trust

16. How do you sanity-check a number before presenting it to leadership?

Look for a concrete checklist, order of magnitude, denominator clarity, cross-checking against a known figure, not just "I double check it."

17. Describe a time (real or hypothetical) where a query returned a technically correct but practically misleading number. What caused it?

Tests real understanding of fan-out, wrong join types, or timezone issues, ideally with a specific, plausible example.

18. What does "grain" mean in the context of a data table, and why does it matter?

Tests whether the candidate understands that getting the grain wrong is a root cause of double-counting and inflated totals.

19. How would you explain to an engineer why a metric definition matters as much as the query itself?

Look for the idea that a technically correct query against a poorly defined metric still produces a number nobody should trust.

20. What's the difference between the data in a production database and the data in a data warehouse, and why does it matter for a PM?

Tests awareness of data freshness and pipeline lag, and the risk of treating a warehouse number as perfectly real-time.

SQL for Product Managers

Module 1Why SQL Is a PM Skill

Module 2Core SQL Every PM Must Know

Module 3Building Real Product Analysis

Module 4Working With Data Like a PM, Not an Analyst

Module 5Advanced Concepts and Working Cross-Functionally

Module 6Write and Run a Real Product Metrics Query Set

Module 725 SQL and Data Interview Questions for Product Managers

Data Quality and Trust