The 8-Point SQL Checklist That Distinguishes Junior Analysts from Senior Engineers

2026-05-23 · Source: Data Engineering on Medium · Field: Technology & Digital — Data Science & Analytics, Software Development & Engineering · Depth: Intermediate, medium

Summary

The 8-Point SQL Checklist outlines critical best practices for data analysts and engineers to ensure accurate query results, especially in high-stakes technical interviews and production environments. This checklist addresses common errors that can lead to inflated metrics, sometimes by 40% or more. Key points include verifying table grain using cardinality checks, prioritizing "WHERE" clauses for early row-level filtering over "HAVING" for aggregate filtering, and understanding their distinct execution order. It also details how to prevent "LEFT JOIN"s from inadvertently becoming "INNER JOIN"s by placing filters correctly within the "JOIN" predicate, and how to audit for duplicate row inflation post-joins. Furthermore, the checklist emphasizes deliberate "NULL" handling with "COALESCE", selecting appropriate window functions like "ROW_NUMBER" for specific deduplication needs, and performing final sanity checks on row counts and key metrics to validate data integrity.

Key takeaway

For data engineers and analysts preparing for technical interviews or deploying production SQL, consistently applying this 8-point checklist will significantly elevate your query accuracy and performance. You should integrate these checks—from validating table grain and optimizing filters to correctly handling joins and NULLs—into your pre-execution routine. This systematic approach not only prevents common data inflation errors but also demonstrates a senior-level understanding of SQL's underlying logic and data integrity, crucial for reliable analytics and robust data systems.

Key insights

Accurate SQL querying demands a systematic pre-execution audit covering data grain, filtering, joins, NULL handling, and aggregate validation.

Principles

• Misinterpreting table grain corrupts aggregations.
• Filter rows early using WHERE for efficiency.
• Place LEFT JOIN filters in the ON clause.

Method

Before executing SQL, perform an 8-point audit: verify table grain, filter early, correctly use WHERE/HAVING, protect LEFT JOINs, check for duplicate inflation, handle NULLs, choose the right window function, and validate final metrics.

In practice

• Use "COUNT(*)" with "HAVING COUNT(*) > 1" for grain checks.
• Apply "COALESCE(column, 0)" for robust NULL arithmetic.
• Filter "ROW_NUMBER() = 1" for deduplicating records.

Topics

• SQL Best Practices
• Data Integrity
• Query Optimization
• SQL Joins
• NULL Handling
• Window Functions

Best for: Data Scientist, Data Engineer, Analytics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Data Engineering on Medium.