10 Common SQL Interview Questions for Entry-Level Data Analysts

The One Skill Every Data Analyst Must Have. Let me ask you a question: what’s the single most common tool mentioned in data analyst job descriptions? If you guessed SQL, you’re right. SQL (Structured Query Language) is the fundamental skill that unlocks the data you need to analyze. It’s how you have a conversation with a database. While the thought of “coding” can be intimidating, mastering basic SQL for data analysis is about learning a logical, powerful way to ask questions. This guide will walk you through the top 10 SQL interview questions for beginners, explaining not just the “what,” but the “why” behind each command. Let’s get you from nervous to ready.

10 Foundational SQL Concepts for Your Interview

1. What is SQL, and why is it important for a data analyst?

Beginner-Friendly Answer: SQL is a programming language designed specifically for talking to and managing data in relational databases (organized, table-based data). For a data analyst, it’s important because it’s our primary tool for data extraction. Before you can clean, analyze, or visualize anything, you need to get the data. SQL allows you to precisely ask the database for the specific records, columns, and filtered information you need for your project. It’s the essential first step in the entire analytics workflow.

2. What are the different types of SQL statements?

Answer: We typically group them by their main job. The big four for analysts are:

• DDL (Data Definition Language): Defines structure (e.g., CREATE a table, ALTER a column). (You’ll use this less often as an analyst).
• DML (Data Manipulation Language): Manipulates the data within tables. This is your core toolkit: SELECT (retrieve), INSERT (add), UPDATE (change), DELETE (remove).
• DCL (Data Control Language): Controls access to data (e.g., GRANT permission). (Usually handled by database administrators).
• TCL (Transaction Control Language): Manages transactions (e.g., COMMIT changes). As a beginner analyst, you will use DML—especially the SELECT statement—constantly.

3. What is the SELECT statement used for?

Answer: SELECT is the single most important SQL command for an analyst. It’s used to retrieve or “fetch” data from a database. You use it to specify exactly which columns of data you want to see from a table. The simplest query is: <strong>SELECT * FROM customers</strong>;, which means “show me all columns from the customers table.” In real work, you almost always specify columns like: SELECT name, email, signup_date FROM customers; to get just the data you need.

4. How do you filter records using SQL?

Answer: You filter records by adding a WHERE clause to your SELECT statement. This is how you answer specific business questions. For example, SELECT * FROM orders WHERE amount > 100; filters to show only orders over $100. It’s like using a filter in Excel, but much more powerful because you can do it across millions of rows instantly.

5. What is the purpose of the WHERE clause?

Answer: The WHERE clause is your filter. Its purpose is to limit the results of your SELECT query to only the rows that meet a specific condition. It allows you to focus your analysis on a relevant subset of data, such as customers from a particular region, sales from last quarter, or products in a specific category. Without it, you’re just getting a massive, unfocused data dump.

6. What is the difference between = and LIKE?

Answer: This is a classic interview question.

• = is used for exact matches. WHERE city = 'Boston' finds only rows where the city is exactly “Boston.”
• LIKE is used for pattern matching with wildcards. WHERE email LIKE '%@gmail.com' finds any email ending with “@gmail.com“. The % symbol is the wildcard that means “any characters.” Use = when you know the exact value; use LIKE when you’re searching for a pattern or partial text.

7. What are aggregate functions in SQL?

Answer: Aggregate functions perform a calculation on a set of rows and return a single summary value. They are the math of SQL. The most common ones every analyst must know are:

• COUNT(): Counts the number of rows.
• SUM(): Adds up the values in a column.
• AVG(): Calculates the average value.
• MIN() and MAX(): Find the smallest and largest values.
  Example: SELECT AVG(salary) FROM employees; gives you the single average salary number.

8. What does GROUP BY do?

Answer: GROUP BY is what allows you to create summary reports. It groups rows that have the same values in specified columns, so you can run aggregate functions on each group.

For instance, SELECT department, COUNT(employee_id) FROM employees GROUP BY department; doesn’t just give you a total count—it gives you a count for each department separately. It transforms raw data into categorized summaries.

9. What is the difference between WHERE and HAVING?

Answer: Another crucial distinction. Both filter data, but they act at different stages.

• WHERE filters individual rows before they are grouped. (WHERE salary > 50000).
• HAVING filters aggregated group results after a GROUP BY. (HAVING COUNT(employee_id) > 10).
  Simple rule: Use WHERE on regular column conditions. Use HAVING on conditions involving aggregate functions (COUNT, SUM, AVG).

10. What is ORDER BY, and why is it useful?

Answer: ORDER BY does exactly what it sounds like: it sorts your results. It’s incredibly useful for making your query output readable and immediately insightful. You can sort alphabetically, numerically, by date, and in ascending (ASC) or descending (DESC) order. Example: SELECT product_name, sales FROM products ORDER BY sales DESC; immediately shows you your top-selling products at the top of the list. It’s essential for presentation and quick analysis.

You’re Ready to Start the Conversation

Mastering these beginner SQL interview questions for a data analyst isn’t about memorizing syntax; it’s about understanding how to logically ask a database for the story hidden in the data. When you can explain the difference between WHERE and HAVING, you show an interviewer you understand the flow of data analysis—from filtering raw details to summarizing grouped results.

What’s next on your journey? These basics are your foundation. In our next guide, we’ll build on this by tackling intermediate SQL concepts like JOINs, which allow you to combine data from multiple tables—where the real analytical power begins.

What was the first SQL query you ever felt proud of writing? Or what concept are you still working to master? Share with the community in the comments below!