How to Pass the Amazon SQL Interview (A Practical Guide)

If you're prepping for a Business Intelligence (BI) Engineer, Data Analyst, Data Engineer, or Data Scientist role at Amazon, you probably already know SQL matters. It's a core part of the hiring bar.

But Amazon isn't just checking your syntax. They want to see if you can think in sets, write clean queries under pressure, and reason about data the way the business actually uses it.

Here's exactly how to prepare based on what the interview actually rewards.

What the Interview Really Tests

Amazon's SQL rounds usually show up in one of two ways. It's either a technical screen using a shared coding tool, or a whiteboarding case-study during the main loop.

Either way, the interviewer is watching for a few specific signals. For starters, correctness always beats speed. A working query is far better than a clever one that fails.

Communication is also huge. Do you talk through your logic before you even touch the keyboard?

You'll often get a vague ask, like finding the "best" customers. You are completely expected to define what "best" means out loud before you start writing Common Table Expressions (CTEs).

And watch out for messy data. Nulls, duplicates, and mismatched grain are almost always baked into the problem on purpose.

The Core Topics to Master

Focus your prep time on a few specific areas. Actually, it turns out this is where almost all the interview questions live.

Joins Inside and Out

You need to know your INNER, LEFT, RIGHT, and FULL OUTER joins cold. Be ready to explain exactly why row counts change after each one.

A classic Amazon-style question is finding customers who placed orders but never left a review. That's just a LEFT JOIN with a NULL check. The interviewers want to see you reach for it right away.

Window Functions

Functions like ROW_NUMBER(), RANK(), DENSE_RANK(), and LAG() or LEAD() show up constantly.

You might see a common pattern—like finding the second-highest order value per customer, or calculating month-over-month growth. If you're shaky here, definitely drill this first.

Aggregations and Grouping Logic

Know the difference between HAVING and WHERE instinctively. You'll also need to get comfortable nesting aggregates inside Common Table Expressions (CTEs).

Amazon likes multi-step problems. You usually have to aggregate first, and then filter or rank whatever you just aggregated.

CTEs Over Subqueries

Interviewers really prefer readable, well-structured queries. A WITH clause that breaks a problem into logical steps reads so much better than a deeply nested subquery.

On top of that, it makes it way easier for you to talk through your thinking out loud.

Date and Time Manipulation

Cohort analysis, retention windows, and rolling averages come up a lot. That makes perfect sense given Amazon's retail and logistics roots.

Practice using DATE_TRUNC, finding date differences, and writing rolling window aggregations.

Handling Nulls and Duplicates

Expect a hidden trap in the data. You'll likely see duplicate order rows, nulls in a join key, or a customer table with more than one entry per person.

The interviewer wants to see if you notice the trap, not just whether your SQL actually runs.

A Simple Framework for the Interview

Start by restating the problem. Repeat back what's being asked, and ask a clarifying question if the metric is ambiguous. For instance, does "active user" mean they just logged in, or did they actually make a purchase?

Talk through your approach before coding. Say out loud what you plan to do. "I'll start by joining orders to customers, then aggregate by customer to get total spend, then rank."

Write in logical blocks. Build the query as a sequence of CTEs rather than one giant block of text. It's easier to debug, and much easier for the interviewer to follow.

Sanity-check your own output. Mention what you'd expect to see—like expecting one row per customer—and verify edge cases like ties or nulls.

Finally, state the tradeoffs. If there's a more efficient approach (like avoiding a self-join), mention it even if you don't have time to rewrite the whole query.

How to Actually Practice

Use real platforms, not just flashcards. Sites like DataCurlew, DataLemur, and LeetCode's database section have Amazon-tagged questions pulled from real interview reports.

Practice on messy data. Don't just query perfectly clean textbook tables. Intentionally practice on datasets with duplicate rows and nulls so handling them becomes automatic.

Time yourself. Most SQL rounds are 30 to 45 minutes including the discussion. Aim to write a correct, readable query in under 15 minutes.

Say your answer out loud, even when you're alone. Talking through logic is a skill in itself, and practicing your narration makes it feel natural instead of forced on the big day.

Common Mistakes That Cost Candidates

Jumping straight to code before clarifying the metric is a huge red flag. So is writing one massive query instead of breaking it down into CTEs.

Ignoring duplicates or nulls will quietly skew your results and instantly fail the test. I've actually seen this happen to incredibly smart candidates.

Going silent while thinking is also dangerous. Interviewers can't evaluate reasoning they can't actually hear.

Always check what grain your COUNT, SUM, or JOIN is operating on. This is a super common source of answers that look right on the surface but are completely wrong underneath.

The Amazon SQL interview isn't about trivia. It's about turning a vague question into a clean, correct query—and explaining your thinking as you go.

Master your joins and window functions. Practice narrating out loud. Get comfortable with messy data. Do that, and you'll be completely ready.