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How to Prepare for a SQL Interview: What Actually Comes Up
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.
CR
Conor Robertson
May 19, 2026 · 10 min read
Frequently asked questions
The questions that come up most about SQL interview prep.
More practical than theoretical. Most entry-level interviews involve one or two tasks given a schema: write a query that answers this business question. You won't be asked to recite syntax or explain database internals. The most common format is a take-home task or a shared coding environment where you write queries against real tables. The bar for entry-level is solid SELECT, WHERE, GROUP BY and at least some comfort with JOINs.
No. That's the wrong frame entirely. You should be comfortable enough with the syntax that it doesn't slow you down, but the goal is writing fluent queries, not reciting them from memory. In most live technical screens you have access to documentation and can look up specific function names. What you can't look up is analytical intuition: how to break a business question into a query structure. Focus on that.
Say so, honestly, and show your working. 'I'm not sure of the exact syntax for that, but here's how I'd approach the problem structurally' is a better answer than silence or a guess that demonstrably fails. Interviewers are watching how you think under uncertainty. Admitting a gap and reasoning through it is stronger than bluffing.
More important than most guides suggest. ROW_NUMBER(), RANK(), LAG(), and LEAD() come up frequently in entry-level screens, particularly at data-forward companies. A candidate who can reach for ROW_NUMBER() to get the latest row per group will stand out from one who writes a messier subquery to do the same thing. Even a surface-level understanding of PARTITION BY and ORDER BY within a window will help.
Less than you'd think for the interview itself. Core SQL (SELECT, JOIN, GROUP BY, subqueries) is nearly identical across PostgreSQL, Snowflake, BigQuery, and others. Dialect differences mostly show up in functions: DATE_TRUNC vs DATE_PART, QUALIFY (Snowflake only), or GENERATE_DATE_ARRAY (BigQuery). Check the job description for the database mentioned and look up one or two differences beforehand, but don't let dialect variations be a reason to delay your prep.
Yes, and it is almost always the right move. Asking 'what does the status column contain?' or 'does this table include cancelled orders?' before writing shows you're thinking like an analyst, not just a query writer. Most interviewers expect and welcome clarifying questions. Candidates who dive straight in without asking anything often produce technically correct answers to the wrong question.
Practice on real questions before the interview.
QueryCase has company-style SQL interview questions you solve in a real editor against a target output, with a worked solution and the insight behind each one. Read the question, write the query, run it.
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."
What Hiring Managers Are Actually Testing
It's worth being direct about what a SQL interview is really measuring. It's not just whether you can write queries. In fact, now that AI drafts syntax in seconds, live SQL rounds are increasingly a test of judgment: can you be trusted to sign off on a number? (We wrote about what AI actually changed for data analysts separately.)
At the entry to mid level, most hiring managers are testing for three things in roughly this order:
01
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.
02
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.
03
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.
The SQL Questions That Come Up Everywhere
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. If you want to drill them the way the interview actually works, the SQL interview questions on QueryCase put each one in a live editor with a schema and a target output to match.
Entry level
Find the top 5 customers by total order value in the last 90 days.
This combines filtering by date, aggregation, and ordering: three things that come up together constantly in real analytics work. The key is not just getting the right answer but writing it cleanly.
SQLExample answer
SELECT
customer_id,
SUM(order_value) AS total_value
FROM orders
WHERE order_date >=CURRENT_DATE - INTERVAL'90 days'GROUP BY customer_id
ORDER BY total_value DESCLIMIT5;
What they're testing
Date filteringGROUP BYAggregationORDER BY + LIMIT
Entry level
Write a query to find users who have never placed an order.
A classic. Tests whether you know how to find the absence of something, which is one of the most common real-world query patterns. There are two valid approaches: LEFT JOIN with a NULL check, or NOT EXISTS. Both are worth knowing.
SQLApproach 1: LEFT JOIN
SELECT u.user_id, u.email
FROM users u
LEFT JOIN orders o ON u.user_id = o.user_id
WHERE o.user_id IS NULL;
SQLApproach 2: NOT EXISTS
SELECT user_id, email
FROM users u
WHERE NOT EXISTS (
SELECT1FROM orders o
WHERE o.user_id = u.user_id
);
What they're testing
LEFT JOINNULL handlingSubqueriesAnti-join patterns
Mid level
For each user, show their most recent order and the value of that order.
This is where entry-level candidates stumble. Getting 'the most recent X per group' requires either a window function or a correlated subquery. Window functions are the cleaner approach and will impress interviewers at most modern data teams.
SQLUsing ROW_NUMBER (the clean approach)
WITH ranked_orders AS (
SELECT
user_id,
order_id,
order_value,
order_date,
ROW_NUMBER() OVER (
PARTITION BY user_id
ORDER BY order_date DESC
) AS rn
FROM orders
)
SELECT user_id, order_id, order_value, order_date
FROM ranked_orders
WHERE rn = 1;
What they're testing
Window functionsCTEsPARTITION BYRanking patterns
Mid level
Calculate the month-over-month percentage change in revenue.
Period-over-period comparisons are one of the most common tasks in any analytics role. LAG() is the elegant solution: it pulls the previous row's value directly, without needing a self-join.
SQLUsing LAG() window function
WITH monthly_revenue AS (
SELECTDATE_TRUNC('month', order_date) AS month,
SUM(order_value) AS revenue
FROM orders
GROUP BY1
)
SELECT
month,
revenue,
LAG(revenue) OVER (ORDER BY month) AS prev_revenue,
ROUND(
(revenue - LAG(revenue) OVER (ORDER BY month))
/ LAG(revenue) OVER (ORDER BY month) * 100, 2
) AS pct_change
FROM monthly_revenue
ORDER BY month;
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 and the OVER clause. 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.
The Part Most Candidates Miss: The Domain Layer
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:
What is their business model, and how do they make money?
What are the two or three metrics their business lives and dies by?
What data does their product almost certainly generate that an analyst would work with?
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.
Calculate the monthly churn rate for paid subscribers
Find users who signed up in January and are still active 90 days later
Which features are used most in the first 7 days after signup?
Calculate the average time from trial start to first payment
What to research beforehand: The pricing page is the best starting point. It tells you whether the company is usage-based, seat-based, or tiered, and each model generates different data and different metrics. A usage-based SaaS cares deeply about consumption patterns; a seat-based one focuses on expansion and contraction.
Your Pre-Interview Prep Checklist
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.
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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.
On the Day: How to Approach a Live SQL Task
The prep is done. Here's how to use it when the screen opens and the timer starts.
1
Read the schema before you write anything
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.
2
Ask one clarifying question before you begin
"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.
3
Start with the simplest version first
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.
4
Narrate your thinking as you work
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.
5
Sanity-check the output before you say done
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 on real interview questions before the day, and it'll show when it counts.
How to Prepare for a SQL Interview: What Actually Comes Up
