SQL shows up in 41% of verified data engineering interview rounds. GROUP BY alone appears in 32% of SQL questions, INNER JOIN in 29%, window functions in 21%. We built 854 challenges that map directly to the concepts interviewers actually ask about, ranked by frequency in a corpus of 1,042 real rounds. You practice what gets tested, not what's trendy.
Of DE rounds test SQL
Phone-screen SQL rounds
GROUP BY frequency
Graded problems
Source: DataDriven analysis of 1,042 verified data engineering interview rounds.
122 onsite rounds and 341 phone screens in our corpus tested SQL output correctness, not pattern-matching on your query text. We mirror that: a live Postgres instance runs your query, and the grader reads actual rows. The numbers below show why that matters.
Every challenge runs your SQL against a live PostgreSQL database. No multiple-choice shortcuts, no regex matching on your answer string. You write a query, it executes, and you see actual rows. If your JOIN condition is wrong, you get wrong rows, not a red X with no explanation. This is the same feedback loop you get in a real interview, where the interviewer watches your query run and asks follow-up questions about the output.
Our challenges are modeled after real data engineering interview questions from companies like Meta, Google, Amazon, and Stripe. Each one gives you a schema description and a business question. No hints about which SQL features to use. You have to read the question, decide on an approach, and write the query from scratch. That mirrors what happens in an actual interview round where nobody tells you 'use a window function here.'
Submit your query and get results in under two seconds. The grader compares your output against the expected result set, checks column ordering, handles NULL edge cases, and flags partial matches. You will know immediately whether your logic is right, partially right, or completely off. No waiting for manual review. No ambiguous 'try again' messages.
Start with single-table SELECT and WHERE clauses, then work through JOINs, aggregations, subqueries, window functions, CTEs, and recursive queries. Each difficulty level builds on the previous one. Beginner challenges focus on syntax correctness. Intermediate problems introduce multi-step logic. Advanced challenges require optimization thinking and handling edge cases that break naive solutions.
Your profile tracks completion rates by topic and difficulty. Spot weak areas at a glance: maybe you are 90% on aggregations but only 40% on window functions. That tells you exactly where to spend your next practice session. The spaced repetition system resurfaces problems you struggled with, so weak spots don't stay weak.
Real interviews have time pressure. Drill mode gives you a random problem and a countdown timer. You practice writing correct SQL under constraints, which builds the kind of automatic recall you need when an interviewer is watching you type. Most candidates who fail SQL interviews know the concepts but cannot write the syntax fast enough.
Each topic covers a specific SQL skill that data engineering interviewers test. The problem counts below reflect our current library. New challenges are added weekly.
GROUP BY is the most frequently tested SQL keyword in data engineering interviews. These problems cover COUNT, SUM, AVG, MIN, MAX, HAVING clauses, conditional aggregation with CASE WHEN, and multi-level grouping. You will practice writing queries that summarize transactional data into the kind of business metrics interviewers ask about: retention rates, revenue breakdowns, conversion funnels.
Practice Aggregation and GROUP BY →INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN, CROSS JOIN, self-joins, and inequality joins. These problems start with straightforward two-table joins and progress to multi-table queries where you need to think carefully about which table drives the result set. Anti-join patterns (LEFT JOIN + WHERE IS NULL) appear frequently in interview settings and are well-represented here.
Practice JOINs →ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, NTILE, SUM OVER, AVG OVER, and frame clauses (ROWS vs RANGE). Window functions are the highest-difficulty topic that consistently appears in DE interviews. These problems cover ranking within partitions, running totals, moving averages, gap-and-island detection, and percent-of-total calculations.
Practice Window Functions →Common Table Expressions for readable multi-step logic, plus recursive CTEs for hierarchical data like org charts, category trees, and bill-of-materials. Chaining multiple CTEs to break complex transformations into testable steps is a pattern that impresses interviewers because it shows you think about code readability, not just correctness.
Practice CTEs and Recursive Queries →Scalar subqueries, correlated subqueries, EXISTS and NOT EXISTS, subqueries in FROM clauses. These problems train you to decide when a subquery is cleaner than a JOIN or CTE, and when it is not. Correlated subqueries are a common interview trap because they look simple but perform terribly on large datasets.
Practice Subqueries →DATE_TRUNC, DATE_DIFF, EXTRACT, interval arithmetic, timezone handling. Time-series analysis is central to data engineering work: calculating retention cohorts, finding gaps in event streams, aggregating into weekly or monthly buckets. These problems reflect the date manipulation you will do daily in production pipelines.
Practice Date and Time Functions →LIKE, regex patterns, CONCAT, SUBSTRING, SPLIT_PART, TRIM, REPLACE, and REGEXP_REPLACE. Data cleaning is a huge part of real DE work, and interviewers test whether you can handle messy input: extracting domains from email addresses, normalizing phone numbers, splitting delimited strings into rows.
Practice String Functions →COALESCE, NULLIF, IS NULL, IS NOT NULL, and the three-valued logic that trips up even experienced engineers. These problems expose the subtle bugs that NULL causes in JOINs, WHERE clauses, aggregations, and CASE statements. Getting NULL handling right is the difference between a query that looks correct and one that actually is correct.
Practice NULL Handling →Solving 500 easy problems is less valuable than solving 60 problems that actually challenge you. Here is a framework that works for most candidates preparing for data engineering SQL rounds.
Week 1: Foundations. Spend the first week on SELECT, WHERE, GROUP BY, HAVING, and basic JOINs. These are not glamorous, but they appear in every single SQL interview. If you have to think about GROUP BY syntax during the interview, you have already lost time you need for the hard parts of the problem. Get these patterns to the point where they are automatic.
Week 2: Intermediate patterns. Move to multi-table JOINs, subqueries, CASE WHEN aggregations, and date functions. These are the building blocks of medium-difficulty interview questions. Practice combining multiple techniques in a single query: a JOIN plus an aggregation plus a HAVING filter. Real interview questions rarely test one concept in isolation.
Week 3: Window functions and CTEs. This is where most candidates hit a wall. ROW_NUMBER, RANK, LAG, LEAD, running totals, and the deduplication pattern (ROW_NUMBER + WHERE rn = 1) are tested in the majority of mid-to-senior DE interviews. Practice until you can write these without looking up the syntax.
Week 4: Timed drills and weak spots. Switch to timed mode. Give yourself 15 minutes per problem. Review your completion rates by topic and spend extra time on anything below 70%. The goal is not perfection; it is reliable execution under pressure.
Run every query. Reading solutions teaches you concepts. Writing and running queries teaches you execution. Interviewers watch you type. They notice when you fix a syntax error without hesitation versus when you stare at the screen trying to remember whether it is PARTITION BY or PARTITIONED BY. Muscle memory comes from repetition, not reading.
Study the edge cases. NULL values in LEFT JOINs. Empty groups after HAVING filters. Window functions on the first row of a partition where LAG returns NULL. These edge cases are what separate a candidate who gets the right answer from one who gets an answer that looks right on the sample data but breaks on production-scale inputs. DataDriven problems include edge cases in the test data specifically to surface these bugs.
Single-table SELECT, WHERE filtering, ORDER BY, LIMIT, basic aggregations. Perfect for anyone who is new to SQL or wants to solidify fundamentals before tackling interview-level problems. Most candidates should be able to solve these in under 5 minutes.
JOINs across 2-3 tables, GROUP BY with HAVING, subqueries, CASE WHEN conditional logic, and basic date functions. This is the minimum bar for passing a SQL phone screen. If you cannot solve intermediate problems consistently, focus here before moving up.
Window functions with complex frame clauses, recursive CTEs, correlated subqueries, self-joins for gap detection, and multi-step transformations. This is the level tested in onsite interviews at top tech companies. Expect to spend 15-25 minutes per problem.
Deduplication patterns, slowly changing dimension queries, sessionization, funnel analysis, and queries that require optimization thinking. These problems simulate the kind of SQL you would write in production data pipelines, not just analytics queries. Senior and staff-level interviews pull from this category.
The 20% of SQL that shows up in 80% of DE interviews, graded against a real Postgres 15 instance.