Common SQL Interview Questions for Data Analysts: A Comprehensive Guide

Common SQL Interview Questions for Data Analysts

Recruiters rely on SQL interviews to measure how well candidates can transform raw data into reliable insights. This guide dissects the most common questions, explains why they matter, and shows you how to ace them.

What Are SQL Interview Questions?

SQL interview questions are practical prompts designed to evaluate your ability to query, manipulate, and optimize data stored in relational databases. Unlike theoretical exams, they test real-world problem-solving: joining messy tables, calculating KPIs, or debugging performance issues on the fly.

Why Interviewers Focus on SQL for Data Analysts

SQL is the lingua franca of analytics. Whether you use Python, R, or BI dashboards, the underlying data retrieval almost always happens in SQL. Mastery demonstrates that you can:

• Generate accurate insights without pulling entire tables into memory-hungry tools.
• Collaborate with engineers and stakeholders by speaking a shared language.
• Optimize queries to keep warehouses (and cloud bills) lean.

Core Concept Categories

Data Retrieval & Filtering

Expect basic SELECT, WHERE, and pattern matching questions. Recruiters look for clean, readable syntax over clever tricks.

Aggregation & Window Functions

Calculating weekly revenue or a customer’s 90-day rolling average tests your understanding of GROUP BY, COUNT(), and window functions like ROW_NUMBER() and LAG().

Joins & Set Operations

Real datasets live in multiple tables. Interviewers probe your fluency with INNER, LEFT, CROSS joins, plus UNION and EXCEPT.

Subqueries & CTEs

Nested queries and Common Table Expressions (CTEs) test readability and the ability to break down complex logic.

Optimization & Performance

Indexes, execution plans, and refactoring for cost efficiency differentiate junior analysts from seasoned pros.

Common Questions and Answers

Below are representative prompts with condensed explanations (details appear in the code section later):

• Second Highest Salary: Given an employees table, return each department’s second highest salary.
• User Retention: Calculate the percentage of first-time users who returned within seven days.
• Duplicate Detection: Identify customers with multiple email addresses.
• Running Totals: Compute day-over-day cumulative sales.
• Index Benefit: Explain how adding a composite index improves a slow query.

Best Practices for Interview Preparation

• Practice Incrementally: Start with HackerRank-style tasks, then graduate to full case studies.
• Write, Don’t Click: Use a text-first SQL editor (like Galaxy) instead of drag-and-drop BI tools to build muscle memory.
• Optimize Out Loud: When whiteboarding, verbalize how you’d examine the execution plan or add an index.
• Refactor: Show alternate solutions—e.g., window functions vs. self-joins—and explain trade-offs.

Common Pitfalls and How to Avoid Them

• Over-Using DISTINCT: Many candidates slap DISTINCT on ambiguous queries. Instead, fix the join or GROUP BY root cause.
• Neglecting NULL Logic: Failing to handle NULL in joins or aggregations leads to inaccurate metrics. Always test edge cases.
• Cramming Syntax: Memorizing without understanding slows you down. Focus on concepts; syntax will follow.

Example Walk-Through

Suppose you’re asked: “Find the second highest salary in each department.” Instead of a correlated subquery, a window function is concise and scalable:

SELECT department_id,
salary AS second_highest_salary
FROM (
SELECT department_id,
salary,
DENSE_RANK() OVER (PARTITION BY department_id ORDER BY salary DESC) AS rnk
FROM employees
) ranked
WHERE rnk = 2;

Explain why DENSE_RANK() beats MAX() with <> logic: it handles duplicate top salaries and reads naturally.

Leveraging Galaxy to Practice SQL

Galaxy’s blazing-fast desktop SQL editor includes a context-aware AI copilot that autocompletes joins, column names, and even explains execution plans in plain English. Create a “Interview Prep” collection, endorse your best solutions, and replay them with different datasets. Because Galaxy stores run history, you can revisit past mistakes and watch your progression.

Conclusion

SQL interviews probe more than syntax—they assess clarity, optimization mindset, and communication. By mastering core categories, rehearsing aloud, and avoiding common pitfalls, you’ll stand out in any data-analyst interview. Tools like Galaxy streamline practice so you can focus on strategy, not boilerplate.