Dataford · SQL Report
The SQL Interview Report 2026
SQL is the one skill nearly every data interview touches — but how much, and how hard, depends entirely on the role. We broke down 638 SQL interview questions to map who gets grilled on it, how tough it gets, and which companies lean on it most.
Summary · Key findings
SQL is the most universal data skill — and the least evenly spread. Almost every data role meets it somewhere, but the weight is wildly uneven. Analysts and engineers live in SQL; data scientists pass through it.
It is the most approachable technical topic in the loop. Only 13% of SQL questions are rated hard, against 35% easy and 52% medium. Solid fundamentals clear most of the bar; the trick questions are the exception.
Nearly four in ten SQL questions are conceptual, not written queries. 38% ask you to explain a window function, a join strategy, or how you would optimize a slow query — reasoning out loud, not just typing a SELECT.
Data Analysts carry the most SQL by a clear margin. A Data Analyst loop leans on SQL roughly twice as heavily as a Data Scientist (66 questions vs 30), with Data Engineers close behind at 44.
Big Tech and fintech lean on it hardest. Meta asks the most SQL of any company, followed by Google, American Express, Uber, and Microsoft — the firms running the largest warehouses ask the most of your query skill.
SQL is the skill candidates assume they understand and interviewers keep using to separate people. Everyone has written a query; far fewer can do it cleanly under a clock while narrating the plan.
So we looked at exactly how SQL shows up in a data interview — which roles meet the most of it, how hard it actually gets, and how often it is a written query versus a question about how a query works. The answer reshuffles where most people should spend their practice time.
The short version: SQL is broad but shallow, role-defining for some and incidental for others, and as much about explanation as execution. The full method is at the end.
By role
Who actually gets grilled on SQL
“You need SQL” is true for everyone and useful for no one. The weight is what matters, and it tilts hard toward analysts and engineers.
A Data Analyst meets roughly twice the SQL of a Data Scientist, and Data Engineers are close behind. If you are interviewing for an analyst or engineering seat, SQL is not a checkbox — it is a large part of the grade. For a data scientist, it is real but rarely the thing that decides the loop.
The shape of it
Approachable, and half about reasoning
Two things make SQL more forgiving than its reputation: most questions are not hard, and a large share are not even about writing a query.
And nearly four in ten are conceptual — explain a window function, justify a join, speed up a slow query — rather than a blank editor.
That second chart is the one most candidates ignore. Drilling query puzzles prepares you for 62% of the topic; being able to articulate why a query is written a certain way is what carries the rest — and it is the part interviewers use to tell a memorizer from someone who understands the engine.
By company
Who leans on it hardest
The companies that ask the most SQL are the ones running the largest data warehouses, where querying at scale is the daily job.
Meta sits at the top, with Google, American Express, Uber, and Microsoft behind it — a mix of Big Tech and fintech where data volume makes fluent SQL non-negotiable. If one of these is your target, weight your practice toward their style of warehouse-scale querying.
Outlook
How to prepare for the SQL round
Match the effort to the role. For analyst and engineering loops, SQL is a primary grade — get fast and clean on joins, aggregation, and window functions, because the volume is high. For data science loops, be solid but do not over-invest; the topic that decides those is usually somewhere else.
And for everyone, practice explaining as well as writing. A surprising share of the round is reasoning out loud, and that is the cheapest edge most candidates leave on the table.
Drill the SQL questions companies actually ask
Hundreds of real SQL questions — from dashboard reporting to large-dataset trends — with worked solutions and difficulty tags.
Practice SQL questionsFAQ
Frequently asked questions
Is SQL important for data interviews?+
For most data roles, yes — it is the most universal technical skill in the loop. But the weight is uneven: a Data Analyst or Data Engineer faces far more SQL than a Data Scientist or ML Engineer, who may see very little.
How hard are SQL interview questions?+
Less brutal than their reputation. Only 13% of SQL questions are rated hard; 35% are easy and 52% medium. Consistent command of joins, aggregation, and window functions clears most of the bar.
Which roles get the most SQL questions?+
Data Analysts get the most by a clear margin — about twice as much SQL as a Data Scientist. Data Engineers are close behind, and Business Analysts see a heavy share too. ML and AI Engineering roles see very little.
Do I need to write SQL or just explain it?+
Both. About 62% of SQL questions are live queries you write, and 38% are conceptual — explaining a window function, a join strategy, or how you would speed up a slow query. Strong candidates can do the second as fluently as the first.
Which companies ask the most SQL?+
Big Tech and fintech lean hardest. Meta asks the most SQL of any company, followed by Google, American Express, Uber, and Microsoft — the firms running the biggest data warehouses.
Methodology
How this report was built
This report draws on 638 published questions in Dataford's SQL & Data Manipulation category, each tagged with a difficulty, an answer format (a written query versus a conceptual explanation), the roles it applies to, and any associated companies.
Role and company figures use those tags; a question can apply to several roles, and company coverage is uneven, so read company counts as relative emphasis rather than a precise league table.
The bank reflects the SQL questions companies ask for these roles as captured and structured by Dataford. Figures are current as of June 2026.