How to Really Nail Your Next FAANG SQL Interview (With a Step-by-Step Mock Breakdown)

Looking for the secret sauce to scoring your dream job at places like Google, Amazon, Meta, Netflix, or Apple? Here’s the good news: You don’t need to be a genius to crack those interviews. You just need to show them how you think through problems, especially in SQL interviews. This post walks you through a real mock SQL interview, points out exactly what interviewers want to hear, and shares straightforward advice you can use right now.

What FAANG Interviewers Want: The Thinking Process Over Answers

You’re probably wondering, does the perfect SQL query matter more than anything else? Not really. At FAANG and top-tier tech companies, getting the “right” answer matters, but it’s rarely enough. Interviewers want to see inside your head. They want to know if you can break down a problem, communicate your approach, and recover when things go sideways.

Why? Because that’s what real work looks like. You rarely tackle a problem once and walk away with the right answer in the real world. You’ll have to work with other engineers, explain your logic, poke holes in your ideas, and piece things together with incomplete info.

Here’s what FAANG interviewers usually look for (and what the Data Engineer Academy will drill into you):

• Thinking process: Don’t just code—explain your logic every step of the way. Say out loud (or comment in your code) what you’re doing and why.
• Clear communication: Interviewers tune in to more than your SQL syntax. They want to know how you’d talk through a problem with coworkers. Clear verbal explanations matter, as do well-written comments in your code.
• Structured breakdown: Top-performing candidates break each problem into smaller steps. They tell the interviewer, “I plan to do X, Y, and Z. Here’s why.” That makes it easy for the interviewer to follow (and jump in if you’re off track).
• Handling pressure: Interviews always come with time limits and tough, layered problems. Mock interviews give you real practice with both soft skills and tough questions.
• Immediate feedback: When you practice in a mock session, you’ll see right away where you stumbled—and how to get better. You’ll get feedback on your approach, communication, and code cleanliness (which matters to hiring teams).

Practicing in this way isn’t just about passing one interview. It’s about building habits that make you a better engineer—and a stronger candidate every time.

If you want resources to keep learning, check out the Data Engineer Academy coursework. There’s plenty for all skill levels and lots of real interview patterns.

Inside a Real SQL Mock Interview: How to Break Down Problems and Succeed

Let’s jump into the mock interview session. You’ll see three classic types of SQL questions that regularly pop up at FAANG companies and other top tech firms. Each section shows how to break it down, what the candidate tried, and what the interviewer really wanted to see.

Problem 1: Find Every User With More Than Five Transactions

Understanding the Data

• User Table: Holds basic info like name and balance.
• Transaction Table: Lists every transaction, with columns for transaction ID, user ID, and the type of service.

You get a question: Find the names of all people with more than five transactions.

Approach and Method

Here’s how the candidate thinks through it:

1. Use the transaction table to count transactions per user.
2. Find all users where that count is greater than 5.
3. Join that list with the user table to get names.

Simple, right? Notice the first thing the candidate does—asks a clarifying question: “Does every row in the transaction table count, or just a certain service type?” The interviewer confirms, every line is a transaction.

SQL Breakdown

Here’s what the solution looks like conceptually:

SELECT 
    u.name
FROM
    users u
JOIN
    (
        SELECT user_id
        FROM transactions
        GROUP BY user_id
        HAVING COUNT(*) > 5
    ) t ON u.user_id = t.user_id

Key tips:

• Use GROUP BY and HAVING to count transactions for each user.
• Then, JOIN the result back to the user table to pull names.

Common mistakes to watch:

• Getting the type of join wrong (inner vs left).
• Miscounting by grouping on the wrong column.
• Not confirming the meaning of “transaction” if unclear.

What Interviewers Want To Hear

• “I’ll start by counting transactions in the second table.”
• “Then I’ll join back to the user table to fetch names.”
• “Let’s check: does every row count, or do we need to filter by service type?”

Communicate your logic out loud or in comments. It shows you get both the big picture and the details.

Problem 2: Show Positive and Negative Balances After All Transactions

The Challenge

Now you need to show—after all the transactions—how many accounts have a positive balance and how many have a negative one. Every transaction is a withdrawal. Also, some accounts have never made a transaction (no history).

This one’s trickier. Even experienced folks mess this up without careful logic.

Step-By-Step Solution

• Start with the accounts table so you keep everyone (even those with no transactions).
• LEFT JOIN the transaction table. This way, accounts with no transactions will have NULLs.
• Sum up the withdrawals for each account. If there are NULLs, treat those as zero so math still works.
• Subtract the total withdrawals from each account’s starting balance.
• Count how many accounts remain in the positive vs negative.

Real-World Code Sketch

SELECT
    SUM(CASE WHEN (a.balance - COALESCE(SUM(t.amount), 0)) > 0 THEN 1 ELSE 0 END) AS positive_accounts,
    SUM(CASE WHEN (a.balance - COALESCE(SUM(t.amount), 0)) < 0 THEN 1 ELSE 0 END) AS negative_accounts
FROM 
    accounts a
LEFT JOIN transactions t ON a.account_id = t.account_id
GROUP BY a.account_id, a.balance

Watch for these pain points:

• LEFT JOIN is key. An INNER JOIN would skip accounts with no transactions.
• When you subtract using NULL, you’ll get NULL a result. Use COALESCE(column, 0) to treat “no transactions” as zero.
• Test your query step-by-step! Run each SELECT as you build it.

Troubleshooting in Action

During the mock, bugs pop up:

• The candidate forgets to handle NULL values, so some balances don’t show up correctly.
• They get the join direction wrong (LEFT/RIGHT).
• They add parentheses in the wrong spots in a CASE statement.

The coach helps by nudging: “Check the math with NULLs. You have to adjust your logic where NULL pops up instead of zero.”

Tip: Always check and explain where errors come from. Even if you’re stuck, talk through your fix. It shows you understand the edge cases.

Problem 3: Identify Users With Three Days of Consecutive Transactions

Problem Setup

Now for the big one: Find users who made transactions for three days in a row, but with only one transaction on the second day. The output should show the name, date, and purchase sequence.

Here’s how you might break this down:

1. For each user, create a sequence of their transaction dates.
2. Use window functions (like LAG or LEAD) to look at previous/following days.
3. Find stretches where each date is exactly one day apart from the previous.
4. Check the number of transactions on the middle day—must be only one.

Key SQL Tools

• LAG(), LEAD()
• PARTITION BY for each user
• ORDER BY, for dates
• DATE_DIFF() to compare dates

Pseudo-Solution Flow

1. Pull all transactions, sorted by user and date.
2. Create a subquery LAG() to hold previous days.
3. Use DATE_DIFF() to check for consecutive days.
4. Count transactions per user per day—filter for only one on the middle day.

WITH consecutive_transactions AS (
    SELECT
        account_id,
        transaction_date,
        LAG(transaction_date, 1) OVER (PARTITION BY account_id ORDER BY transaction_date) AS prev_date,
        LAG(transaction_date, 2) OVER (PARTITION BY account_id ORDER BY transaction_date) AS prev2_date
    FROM
        transactions
)
SELECT
    -- select appropriate fields after filtering for three consecutive days
FROM
    consecutive_transactions
WHERE
    DATE_DIFF(transaction_date, prev_date, day) = 1
    AND DATE_DIFF(prev_date, prev2_date, day) = 1
    -- add logic to check transaction count on prev_date

Watch Out For

• SQL quirks with using aliases created in window functions (can’t always reference in the same SELECT).
• The order of data needs to be sorted by user, then by date.
• Common mistake: forgetting to check the count of transactions on that critical second day.

Interview Tips In Action

• Always state your assumptions: “Does ‘consecutive’ mean calendar days or just days with any activity?”
• If the query returns no data, check your joins and filters.
• When you get stuck, focus on a smaller piece: just figure out the date comparison before adding counts.

If the function gives an error, try breaking the query apart and testing each section. Make fixes one at a time and say what you’re changing.

Best Practices for SQL Interviews at Top Tech Companies

Here’s your checklist for crushing SQL interviews at companies like Google, Amazon, and Meta.

Make Your Thought Process Crystal Clear

• Always say out loud, or write in comments, what you plan to do before you do it.
• Use plain language. Explain your big-picture steps and your detailed logic.
• Ask clarifying questions early—don’t be afraid to check what a vague description means.

Test Bit by Bit, Not Just at the End

• Build your query in steps. Run it every few lines.
• Watch for NULL values—handle them with COALESCE or IS NULL logic.
• Use sample data to check if your output matches your expectations.

Write Easy-to-Read Code

• Stick to uppercase for SQL keywords (SELECT, FROM, WHERE, etc.).
• Put each SELECT field on a new line—it’s easier on the eyes and for feedback.
• Keep things simple. Don’t chain too many complicated joins together. Split into common table expressions (CTEs) if needed.

Use Mock Interviews as Real Practice

• Treat each mock as a “real” interview—set a timer, focus, and don’t cheat by looking up answers midway.
• Review every bit of feedback. Zero in on things like correctly choosing join types, using window functions, or writing clean code.
• Practice each week. Regular short sessions are better than cramming once in a while.

Follow this checklist each time, and you’ll keep sharpening your SQL interview edge.

Real Interview Feedback: How to Apply What You Learn

As important as practice is, feedback is where the real growth happens. Here’s what the interviewer focused on during the mock session, and how you can use it to move forward.

Positive Takeaways:

• Completed every question within the set time.
• Asked smart, clarifying questions and explained each approach.
• Didn’t just code blindly—paused to test logic bit by bit.
• Found the right answers, even when the path wasn’t smooth.

Areas Where You Can Improve:

• Code formatting: use capital letters for functions and break lines for clarity.
• Join types: be 100% sure if you need an INNER JOIN, LEFT JOIN, etc.
• Date functions and window functions: practice until you can write them confidently.
• Don’t just glance at one row of data—check a few examples to be sure your logic holds.

Practical Next Steps:

• Write down your approach for each practice problem. Compare your results to your notes.
• After each mock or real interview, jot down what went well and what you want to fix.
• Keep scheduling mocks—full or mini versions—to check on your progress.

Don’t panic if you’re not perfect. It’s about progress, not perfection.

Keeping Your SQL Skills Sharp and Interview-Ready

You passed one mock. Great! But the secret to getting hired at a FAANG company—or staying sharp—is regular, low-pressure practice.

Here’s how to do it:

1. Practice SQL once a week. Even one or two problems are enough to stay fresh.
2. Mix it up. Try easy, medium, and tough questions. Challenge yourself, but don’t burn out.
3. Set a timer sometimes. Feeling the pressure helps you get comfortable with the real thing.
4. Look for feedback. Use online forums, peers, or coaches from programs like Data Engineer Academy to spot your blind spots.
5. Keep a notebook. Write down your go-to strategies, common mistakes, and lessons from every mock. Review it before your next interview.

Consistency wins over quantity. A bit each week keeps your skills ready for anything.

More Resources to Level Up Now

If you want to keep building, here’s where you can go next:

• Get hands-on with structured courses and advanced interview training through the Data Engineer Academy coursework.
• If you’re ready for personalized help, book a call with a data interview coach and get feedback tailored to you.
• For another real-world example, check out a video with a mock SQL interview breakdown.

Stay active in a data community. Drop your questions in Slack channels or forums, and keep learning with others on the same path.

Whether you’re just starting or you’ve been at it for a while, remember: Interviewing is a skill you build. Every session—real or mock—makes you better. Communicate your process, stay curious, and keep practicing those queries. The next FAANG offer could be closer than you think.

Real stories of student success

▶

Student TRIPLES Salary with Data Engineer Academy

▶

DEA Testimonial – A Client’s Success Story at Data Engineer Academy