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Generic SQL prep misses the point entirely. Here's what hiring managers are really testing for, and why the company you're interviewing at should change how you prepare.
Conor Robertson
May 19, 2026 · 10 min read
The questions that come up most about SQL interview prep.
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Walk into a SQL interview at a SaaS company having memorised the difference between INNER and OUTER JOINs, and you might pass the technical screen. Walk in knowing how to calculate monthly churn, trial-to-paid conversion, and feature adoption rate in SQL, and you've just become the candidate they remember.
Most SQL interview prep advice is generic by design. It covers the syntax and concepts that apply everywhere, which means it gives you no advantage anywhere. The candidates who get offers aren't just technically capable. They've done enough homework to make the interviewer feel like they already understand the business.
This post is about both. The SQL fundamentals that come up in almost every data analyst interview, and the domain-specific layer that separates prepared candidates from impressive ones.
"The candidates who get offers aren't just technically capable. They've done enough homework to make the interviewer feel like they already get the business."
It's worth being direct about what a SQL interview is really measuring. It's not just whether you can write queries.
At the entry to mid level, most hiring managers are testing for three things in roughly this order:
Can you actually write SQL?
Not from memory, but with a schema in front of you. This is the baseline. Surprisingly, many candidates can't do it under slight time pressure on an unfamiliar table structure.
Do you think about data like an analyst?
The difference between someone who passes a technical test and someone who gets hired is whether they ask clarifying questions, think about edge cases, and frame their queries around a business question rather than just producing correct output.
Do you understand the domain?
This is the multiplier. A candidate who uses domain-relevant terminology, asks sensible questions about the data model, and references metrics the company actually cares about signals both genuine interest and genuine preparation. That's rare.
What interviewers notice
The most common feedback hiring managers give about strong data candidates isn't "they wrote perfect SQL." It's "they asked the right questions before they started writing." Pausing to ask "is this the users table or the accounts table?" or "should I include trial users here?" signals analytical thinking, not hesitation.
Before getting to domain-specific prep, these are the questions and query patterns that appear consistently across data analyst interviews at virtually every company. You should be able to write these without hesitation.
Find the top 5 customers by total order value in the last 90 days.
Write a query to find users who have never placed an order.
For each user, show their most recent order and the value of that order.
Calculate the month-over-month percentage change in revenue.
The pattern to notice
Every question above can be answered with a combination of the same six building blocks: SELECT/WHERE, GROUP BY, JOIN, subqueries/CTEs, and window functions. The questions get harder not because the syntax changes but because you need to combine these tools in less obvious ways. Practice the combinations, not just the individual concepts.
Here's what happens in most SQL interviews that go badly: the candidate writes technically correct queries but uses generic column names, asks no questions about the data model, and produces output that could belong to any company in any industry.
Here's what happens when they go well: the candidate walks in having researched what the company actually measures, and speaks that language from the first question.
A hiring manager at a SaaS company who hears a candidate say "I'd want to understand how you're defining active users here. Is it based on logins or feature interactions?" is immediately more engaged than one who hears "I'll just write a SELECT on the users table." Same query. Completely different impression.
Before any interview, spend 30 minutes answering these three questions about the company:
Then look up the terminology for those metrics. Not to memorise formulas, but to use the words naturally in conversation. Select your industry below to see what this looks like in practice:
SaaS & Tech Platforms
Subscription revenue, user engagement, and product analytics. Companies like Notion, Slack, HubSpot, or any B2B software product.
Terms to know
Likely interview questions
Most interview prep is scattered. This checklist covers the specific things worth doing in the week before a data analyst SQL interview. Check them off as you go.
Research the company's business model
Understand how they make money: subscription, transaction fees, advertising, marketplace take rate.
Identify the 2-3 metrics they live by
Google "[company] KPIs" or "[industry] key metrics data analyst". Twenty minutes of reading pays off in the room.
Learn the domain terminology
Use the domain guide above. Aim to use terms naturally in conversation rather than reciting definitions.
Practice writing queries without autocomplete
Most technical screens use plain text editors or shared notebooks. Practice there, not in an IDE that finishes your sentences.
Prepare two or three questions to ask them
"What does your data model look like?" and "What's the biggest data quality challenge you face?" signal analytical thinking.
Review the job description for tool mentions
Snowflake, dbt, BigQuery, Looker: each has syntax quirks. Check one or two differences beforehand.
Have a story ready about a real dataset
"Tell me about a time you used data to find something interesting" is almost universal. Practised answers feel practised.
The mindset shift
Stop preparing to pass a test. Start preparing to have a conversation. The best SQL interviews feel like a discussion between two people who care about the same data problem, not an examination. The domain research and the practice queries both serve the same goal: making it feel like you've already been thinking about their data for weeks.
The prep is done. Here's how to use it when the screen opens and the timer starts.
Spend 60 seconds understanding which tables exist and how they relate before writing a single line. A query built against the wrong table is worse than a slow start.
"Should cancelled orders be included here?" shows analytical thinking. Most interviewers expect and welcome it. Candidates who skip this often produce a correct answer to the wrong question.
Write a working query with basic logic, then add edge case handling. Partial credit is real: a half-finished but coherent query is worth more than a complete broken one.
Don't type in silence. "I'm joining on customer_id here because..." lets the interviewer follow along and course-correct if needed. They're evaluating how you think, not just what you produce.
Does the number make sense? A 100% churn rate is almost certainly wrong. Three rows when you expected hundreds is a red flag. Look at the result critically before declaring it finished.
"Interviewers aren't just evaluating the final query. They're watching how you think."
The candidates who stand out aren't always the fastest. They're the ones who slow down at the right moment: to ask the right question, to read the schema properly, to check whether the output makes sense. Practice that habit before the interview, and it'll show on the day.