How to Ace Your Next SQL Interview!

Hey there! If you’re aiming for a data role, there’s one technical hurdle you can’t avoid , the SQL interview. After being on both sides of the interview table, I’ve seen what separates those who land offers from those who don’t.

In this edition of The Data Hustle, I'll break down exactly how SQL interviews work and what they're actually testing for (beyond syntax), and tactical approaches that have helped me and others land offers.

How SQL Interviews Actually Work

There are typically two types of rounds where your SQL skills get put to the test:

The Initial Screen

This is your first hurdle - usually 30-40 minutes of SQL questions. The goal here is simple: prove you actually know SQL beyond just SELECT * FROM table.

Here’s what they’re really checking:

• Understand the basics (filtering, joining, aggregating)

• Can translate a business question into SQL

• Write readable code that another human could understand

💡 Pro tip: Thought process matters more than perfect syntax. I’ve seen candidates with small syntax errors move forward just because they explained their thinking so clearly.

The Deep-Dive Round

This is where things get serious - a 45-60 minute deep dive that tests your analytical thinking and problem-solving. When they conduct these interviews, they’re specifically looking to see if you can:

1. Handle ambiguity in requirements (because real-world data problems are rarely clear-cut)

2. Consider edge cases and data quality issues

3. Balance performance with readability

4. Communicate your thought process while coding

💡 Pro tip: The top performers? They don’t just start coding — they first confirm they’re solving the right problem.

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Let's get specific about what you'll face in each round.

The Initial Screen: What You Actually Need to Know

In my experience, these screens focus on four key areas:

1. Data Filtering & Aggregation

You'll get questions about slicing data and calculating metrics. For example, finding the average order value by month for premium customers. This tests your ability to use WHERE conditions, GROUP BY clauses, and aggregate functions.

2. Multi-table Operations

Virtually every screen has a question requiring JOINs. A common pattern is finding mismatches between tables, like products that have never been purchased or users who registered but never completed an action.

3. Time-Series Analysis

This is where data science SQL diverges from general SQL interviews. You'll be asked to analyze patterns over time, like calculating month-over-month growth rates or identifying trends in user behavior.

4. Subquery Utilization

These questions test if you can break down a complex problem. For instance, finding customers who spend more than average requires first calculating the average order value, then comparing individual customers against it.

Pro tip: When screening the candidates, they're looking for clean, readable code. They'd rather see a slightly less optimal solution that's well-structured than a mess of nested subqueries that might run marginally faster.

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The Deep Dive: Where Many Candidates Stumble

This is the part where many strong SQL coders fall short. You need analytical skills, not just syntax knowledge.

1. Analytical Window Functions

Window functions are where they separate candidates who memorized SQL from those who understand analytics. Here's a simplified example of using RANK to identify top products within each category:

When interviewing, they always ask candidates to explain the difference between RANK, DENSE_RANK, and ROW_NUMBER. So many people use these interchangeably without understanding how they handle ties.

❗Tip: If you forget syntax, don’t freeze — explain what you're trying to do and how you’d look it up.

2. Cohort Analysis

This is a data science favorite that shows you understand user behavior over time. Instead of just counting users, you're tracking how specific groups behave over their lifecycle with your product.

Candidates struggle with cohort analysis because it requires both technical SQL skills and product intuition. The key insight is organizing users into cohorts (usually by signup date) and then tracking their behavior across different time periods.

3. Funnel Analysis

This question is given several times because it reveals how you think about user journeys. The most common mistake candidates make is treating each funnel step as independent rather than connected.

4. A/B Test Analysis

This shows if you can connect SQL to the actual business impact:

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Most Common Reasons People Fail (I've Seen These Repeatedly)

Having sat through multiple SQL interviews, here's where candidates stumble:

1. Not Asking Clarifying Questions

Seriously, this is the #1 killer. A lot of candidates spend 15-20 minutes solving the wrong problem because they don’t confirm what metric they are actually looking for. Always spend the first 2-3 minutes asking questions. It saves time and shows good communication skills.

Questions I always ask when interviewing:

• "What's the exact definition of [key metric] in this context?"

• "How should we handle duplicates/nulls in this scenario?"

• "What time period should we analyze?"

2. Writing Performance Disasters

Watch out for these red flags:

• Using DISTINCT when you could use proper joins

• Putting complex calculations in WHERE clauses

• Not considering the size of tables when joining

• Using correlated subqueries for problems better solved with joins

3. Poor Communication During Coding

Silent coding is an interview killer. They want to hear your thought process. Talk through what you're thinking, even if it's just "I'm going to join these tables first, then filter the results because..."

What's Worked for Me Personally

These approaches have consistently helped me:

1. I Start With a Clear Framework

Before typing a single line of SQL, I say something like: "Let me break this down. It looks like we need to [restate problem], which means I'll need to [describe approach]. Does that approach make sense before I start coding?"

This has saved me countless times from going down rabbit holes.

2. I Use CTEs to Show My Work

Common Table Expressions make your thought process visible and your code readable.

3. I Connect Back to Business Impact

After solving the problem, I always add something like: "This query would help the business understand [business insight]. If we wanted to take this further, we could also analyze [related dimension] to get deeper insights."

4. I Ask Targeted Questions When Finished

A trick that's worked well for me: when I finish a solution, I ask the interviewer, "Would this approach handle the scale of data you typically work with?" This shows I'm thinking about practical implementation, not just textbook solutions.

Interview Day Tactics That Actually Help

These are personal tactics that have helped me manage interview stress:

1. Research your interviewer: I always look up the LinkedIn profile of my interviewer before the session. Knowing their background helps me relate to them and understand what they might value in responses.

2. Manage interview anxiety: Try taking a walk or doing some quick exercise before the interview.

3. Make a human connection. Technical interviews don't have to be cold. I've found that making a genuine connection with the interviewer, even just a comment about something in their background or a light joke, can make the whole experience more pleasant and get them on your side.

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A Final Thought: It's Not Just About SQL

Hiring Managers are not just testing SQL skills. They’re looking for analytical thinking, problem-solving approaches, and communication style.

So remember - yes, you need to know your SQL, but don't get so caught up in the syntax that you forget to show how you think. That's what ultimately gets you the job.

Best of luck for everything!

- Sai Bysani, a fellow Hustler!

Keep grinding, keep growing,

The Data Hustle.