The SQL round is the single most common round in a data engineer interview. Across 1,042 interview reports we collected, 95% of loops contained at least one live SQL round, and 32.7% of all rounds in the dataset were a phone-screen SQL. This page is part of the our data engineer interview prep hub, which covers every round in the loop. If you skip preparation here, the loop ends at the screen.
The format is consistent across companies. Frequency numbers below come from 1,042 verified interview reports collected on DataDriven from 2024 to 2026.
| Pattern | Share of SQL Questions | Common In |
|---|---|---|
| GROUP BY with HAVING | 15.3% | Every loop, all levels |
| INNER and LEFT JOIN | 21.1% | Every loop |
| Window functions (PARTITION BY) | 9.7% | L4+, FAANG |
| ROW_NUMBER deduplication | 6.2% | Every loop |
| RANK and DENSE_RANK | 4.9% | L4+ |
| Self join on inequality | 4.1% | L4+, FAANG |
| Gap and island problems | 3.8% | Senior, FAANG |
| Rolling and moving averages | 3.6% | Analytics-heavy roles |
| Recursive CTE | 2.7% | Senior, modeling-heavy roles |
| NULLIF and COALESCE patterns | 5.1% | Every loop |
| Date trunc and date math | 7.8% | Every loop |
| EXISTS vs IN | 2.4% | Optimization rounds |
| Pivot with conditional aggregation | 3.2% | Every loop |
| EXPLAIN plan reading | 2.1% | Senior+, optimization rounds |
Each problem below appeared in at least three reported interview loops in the last 18 months. The solution shows the query, the gotchas the interviewer is testing for, and the follow-up question they will ask next.
WITH ranked AS (
SELECT
*,
ROW_NUMBER() OVER (
PARTITION BY user_id
ORDER BY event_ts DESC, event_id DESC
) AS rn
FROM user_events
)
SELECT user_id, event_ts, event_id, payload
FROM ranked
WHERE rn = 1;WITH daily AS (
SELECT DISTINCT user_id, event_date
FROM user_events
),
grouped AS (
SELECT
user_id,
event_date,
event_date - INTERVAL '1 day' *
ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY event_date)
AS streak_key
FROM daily
)
SELECT user_id, MIN(event_date) AS streak_start, COUNT(*) AS streak_days
FROM grouped
GROUP BY user_id, streak_key
HAVING COUNT(*) >= 3;SELECT
order_date,
daily_revenue,
CASE
WHEN ROW_NUMBER() OVER (ORDER BY order_date) >= 7
THEN AVG(daily_revenue) OVER (
ORDER BY order_date
ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
)
END AS rolling_7d_avg
FROM daily_revenue_summary
ORDER BY order_date;SELECT e1.id AS employee_a, e2.id AS employee_b, e1.manager_id FROM employees e1 JOIN employees e2 ON e1.manager_id = e2.manager_id AND e1.id < e2.id;
WITH RECURSIVE org AS ( SELECT id, manager_id, name, 0 AS depth FROM employees WHERE id = :target_manager_id UNION ALL SELECT e.id, e.manager_id, e.name, o.depth + 1 FROM employees e JOIN org o ON e.manager_id = o.id WHERE o.depth < 20 ) SELECT id, name, depth FROM org WHERE id != :target_manager_id;
This is a hot take. Most candidates rush to window functions because they look senior. In our review of recorded mock interviews on DataDriven, 62% of L4 candidates who reached for a window function on a problem solvable with GROUP BY took 4 minutes longer and made more syntax errors than candidates who used GROUP BY first.
Use the simplest construct that produces the right answer. If the problem reduces to one row per group with no per-row context, GROUP BY is correct. If you need a value from a non-aggregated row alongside an aggregate (e.g., last login alongside total logins), that is when window functions earn their complexity. The interviewer wants the right tool, not the fanciest tool. The same judgment shows up in vanilla Python patterns interviewers test, where reaching for pandas on a problem that needs a dict is the equivalent failure mode.
SQL fluency is the entry condition for the rest of the loop, but the patterns recur in surprising places. Gap-and-island problems reappear inside vanilla Python patterns interviewers test as sessionization questions. ROW_NUMBER deduplication shows up again inside star schema and SCD round prep when you argue for SCD Type 2 over Type 1. EXPLAIN-plan reading and partition pruning are the natural lead-in to system design framework for data engineers, where the same instincts scale up to data warehouse design.
Take-home assignments often bundle SQL with a small modeling task, which is why the data engineer take-home prep overlaps here. And if you're targeting a specific company, the live SQL round at Stripe leans hard on financial-precision SQL while the loop at Airbnb tests experimentation-platform SQL. Read the company-specific guides after this one.
The SQL dialect of the round usually mirrors the company stack. Snowflake-heavy companies test Snowflake-specific functions (QUALIFY, FLATTEN). BigQuery-heavy companies test ARRAY_AGG and STRUCT manipulation. Postgres companies stay portable. The underlying patterns transfer, but the syntax does not. If you already know the company stack, drill the right dialect: see BigQuery internals interview prep, Redshift internals interview prep, PostgreSQL internals interview prep, or our SQL question reference for Data Engineer interviews reference for the full set.
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