SQL for Data Science Jobs: What to Master Before You Interview

Most data science jobs still test SQL in screening or onsite rounds. Treat SQL as a first-class skill, not a side note to Python.

Core Topics

Joins (inner, left, and when they lie to you), aggregations, GROUP BY with HAVING, subqueries versus CTEs, window functions (ROW_NUMBER, LAG, SUM OVER), and deduping patterns. Practice explaining your query plan in plain English for reviewers.

Metrics That Show Up

Retention cohorts, conversion funnels, revenue over time, sessionization, and growth accounting. Be able to define an active user without hand-waving.

Performance Intuition

When an interviewer asks about a slow query, think selective filters, indexes, partition keys, and avoiding explosive joins. You do not need DBA depth everywhere, but basic performance awareness helps.

How to Practice

Use free warehouse sandboxes or local DuckDB. Rebuild KPIs from public datasets. Time yourself. Read solutions after, then rewrite from memory.

Pairing SQL With Python

Many roles expect pandas or PySpark after SQL extracts. Show you can hand off clean intermediate tables to modeling code.

Job Search

Filter for roles that say SQL explicitly if you want analytics-heavy positions. Read descriptions for "experimentation" if you want causal and A/B work on top of SQL.

Use job boards that categorize data and analytics roles separately from generic engineering listings.