A curated overview of the SQL topics and questions most frequently asked in data-analyst interviews, with explanations, examples, and preparation tips.
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.
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.
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:
Expect basic SELECT
, WHERE
, and pattern matching questions. Recruiters look for clean, readable syntax over clever tricks.
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()
.
Real datasets live in multiple tables. Interviewers probe your fluency with INNER
, LEFT
, CROSS
joins, plus UNION
and EXCEPT
.
Nested queries and Common Table Expressions (CTEs) test readability and the ability to break down complex logic.
Indexes, execution plans, and refactoring for cost efficiency differentiate junior analysts from seasoned pros.
Below are representative prompts with condensed explanations (details appear in the code section later):
employees
table, return each department’s second highest salary.DISTINCT
on ambiguous queries. Instead, fix the join or GROUP BY
root cause.NULL
in joins or aggregations leads to inaccurate metrics. Always test edge cases.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.
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.
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.
SQL remains the backbone of data analytics. Demonstrating fluency during interviews proves you can translate business questions into performant, accurate queries—critical for driving decisions, keeping cloud costs low, and collaborating across data, product, and engineering teams.
Start by clarifying requirements, outline your approach verbally, write the query step-by-step, and finish by explaining test cases and performance considerations.
Joins, aggregations, window functions, and subqueries top the list. Optimization concepts like indexes add bonus points.
Galaxy’s AI copilot autocompletes joins, explains errors, and keeps a history of your practice sessions. Collections let you organize and endorse your best solutions for quick revision.
No. Focus on core patterns; looking up rare functions is acceptable in real work. Interviewers care more about logical thinking and clean structure.