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SQL for Data & ML Interviews: JOINs, Window Functions, and Query Optimization
Everything you need to solve SQL interview problems at data scientist, ML engineer, and data analyst roles. Covers JOINs and aggregations, window functions (ROW_NUMBER, LAG/LEAD, running totals), CTEs, NULL traps, and the query optimization patterns that separate strong from weak SQL answers.
Why SQL Is Still a Core Interview Skill
SQL appears in almost every data scientist and ML engineer interview loop — not as a gotcha, but because it is the primary language for exploring data, validating features, and debugging pipelines. Interviewers are not testing syntax memorization. They are testing whether you can translate a business question ("find users who were active 3 days in a row") into a correct, readable query.
The three skills that separate strong SQL answers from weak ones:
- Window functions: candidates who only know GROUP BY will fail half of analytics SQL questions. Window functions let you compute rankings, running totals, and row-level comparisons without collapsing rows — essential for retention, session detection, and funnel analysis.
- CTE fluency: writing readable multi-step logic as named CTEs instead of nested subqueries signals engineering maturity. Interviewers can follow your reasoning step by step.
- NULL awareness: SQL's three-valued logic (TRUE / FALSE / NULL) bites everyone who doesn't think about it explicitly. A JOIN that loses rows, an aggregation that counts wrong, a CASE WHEN that silently misbehaves — all trace back to NULL.