How to prepare for SQL interview?

This article covers comprehensive SQL interview preparation with real-world examples, code solutions, and best practices.

How to prepare for Data Analyst interview?

This article covers comprehensive Data Analyst interview preparation with real-world examples, code solutions, and best practices.

How to prepare for Interview Prep interview?

This article covers comprehensive Interview Prep interview preparation with real-world examples, code solutions, and best practices.

How to prepare for Data Analytics interview?

This article covers comprehensive Data Analytics interview preparation with real-world examples, code solutions, and best practices.

How to prepare for Window Functions interview?

This article covers comprehensive Window Functions interview preparation with real-world examples, code solutions, and best practices.

30 SQL Interview Questions for Data Analysts (2026) — With Answers

📌Why SQL Decides Your Data Analyst Interview

Almost every Data Analyst interview has a SQL round, and it's where most candidates get rejected. The good news: the questions are predictable. Master the patterns below and you'll walk in confident.

These are the real questions asked at companies like Swiggy, Razorpay, Meesho, Amazon, Flipkart and top service companies — grouped from basics to window functions.

📌Basics: Filtering & Sorting

Q1. What is the difference between WHERE and HAVING?

WHERE filters rows before grouping; HAVING filters groups after GROUP BY (and can use aggregate functions).

Q2. Difference between DISTINCT and GROUP BY?

Both remove duplicates. GROUP BY is used when you also need aggregations (COUNT, SUM). DISTINCT just returns unique rows.

Q3. Find the second-highest salary.

SELECT MAX(salary) AS second_highest
FROM employees
WHERE salary < (SELECT MAX(salary) FROM employees);

Or, more robustly, with a window function (see Q22).

📌Joins

Q4. Explain INNER, LEFT, RIGHT and FULL JOIN.

INNER — only matching rows in both tables

LEFT — all rows from the left table + matches

RIGHT — all rows from the right table + matches

FULL — all rows from both, matched where possible

Q5. What is a self join? Give a use case.

A table joined to itself — e.g. finding employees and their managers from the same employees table.

Q6. Customers who never placed an order.

SELECT c.customer_id, c.name
FROM customers c
LEFT JOIN orders o ON c.customer_id = o.customer_id
WHERE o.order_id IS NULL;

📌Aggregations & GROUP BY

Q7. Total revenue per month.

SELECT DATE_TRUNC('month', order_date) AS month,
       SUM(amount) AS revenue
FROM orders
GROUP BY 1
ORDER BY 1;

Q8. Top 3 products by sales.

SELECT product_id, SUM(amount) AS sales
FROM order_items
GROUP BY product_id
ORDER BY sales DESC
LIMIT 3;

Q9. What does COUNT(*) vs COUNT(column) do differently?

COUNT(*) counts all rows; COUNT(column) ignores NULLs in that column.

📌Subqueries & CTEs

Q10. What is a CTE and why use it?

A Common Table Expression (WITH) is a named temporary result set that makes complex queries readable and lets you reference it multiple times.

Q11. Categories with above-average order value.

WITH cat_avg AS (
  SELECT category, AVG(amount) AS avg_amt
  FROM orders GROUP BY category
)
SELECT * FROM cat_avg
WHERE avg_amt > (SELECT AVG(amount) FROM orders);

Q12. Correlated vs non-correlated subquery?

A correlated subquery references the outer query and runs per row; a non-correlated one runs once independently.

📌Window Functions (The Difference-Maker)

Q22. Second-highest salary per department.

SELECT department, salary
FROM (
  SELECT department, salary,
         DENSE_RANK() OVER (PARTITION BY department ORDER BY salary DESC) AS rnk
  FROM employees
) t
WHERE rnk = 2;

Q23. Difference between ROW_NUMBER, RANK and DENSE_RANK?

ROW_NUMBER — unique sequential number, no ties

RANK — same rank for ties, skips next numbers

DENSE_RANK — same rank for ties, no gaps

Q24. Running total of daily sales.

SELECT order_date,
       SUM(amount) OVER (ORDER BY order_date) AS running_total
FROM daily_sales;

Q25. Month-over-month growth with LAG.

SELECT month, revenue,
       revenue - LAG(revenue) OVER (ORDER BY month) AS mom_change
FROM monthly_revenue;

📌How to Prepare (and Actually Pass)

Reading answers isn't enough — you must write queries by hand. Practice on real schemas, explain your logic out loud, and rebuild each pattern from scratch. Analytical questions (cohorts, funnels, retention) increasingly use window functions, so get very comfortable with them.

Our SQL for Data Analytics course drills exactly these patterns with real analytical case studies and mock interviews. For the complete analyst skill set — SQL plus Excel, statistics, Power BI and Tableau — see the Data Analytics course.

Master these 30 patterns and the SQL round becomes the easiest part of your interview.