The FDE SQL Interview: What to Expect and How to Prepare
Forward Deployed Engineers live in the customer's data, so nearly every FDE loop includes a SQL round, and it is more demanding than a basic SELECT/WHERE screen. Expect multi-step transformations, window functions, and questions that reward you for reasoning about data correctness.
What the FDE SQL round tests
The round is less about trivia and more about whether you can produce correct results from realistic, imperfect data. Interviewers watch for a few specific habits.
Grain and join correctness
The single most common mistake is silent fan-out: joining a one-to-many relationship and double-counting a metric without noticing. Strong candidates state the grain of every table, "one row means one order line", before joining, and sanity-check row counts after each step.
Window functions
ROW_NUMBER, RANK, and SUM/AVG OVER (...) answer "top N per group" and running totals without collapsing rows the way GROUP BY does. This is the single biggest separator in FDE SQL rounds.
CTEs and readability
Real transformations are multi-step. Breaking them into named CTEs shows you can write SQL a customer's team could maintain, which is the job.
NULL handling
NULL never equals anything (use IS NULL), and aggregates silently skip NULLs. A careless NULL is a classic wrong-answer trap.
Common question shapes
A few templates cover most FDE SQL rounds. Recognizing which one you are handed is half the battle.
Top-N per group
"The three highest-value orders per customer." A window function (ROW_NUMBER or RANK partitioned by the group, ordered by the metric) inside a CTE, then filter to rank <= N. Reaching for GROUP BY alone here is the trap.
Running totals and moving averages
"Cumulative revenue by day" or a "7-day rolling average." SUM or AVG OVER (ORDER BY date ROWS BETWEEN ...). Be explicit about the window frame; the default frame surprises people.
Cohort and retention
"Of users who signed up in January, how many were active each later month?" Anchor each user to their first event, then join activity back by month offset. It tests join grain and date bucketing at once.
Deduplication and gaps
Collapsing duplicate rows to the latest version, or finding gaps in a sequence. ROW_NUMBER picks one row per key; LAG and LEAD compare a row to its neighbor.
How to prepare
Practice against a real database, not flashcards; the muscle memory of writing and running queries is what the round tests. Work through GROUP BY with HAVING, multi-table joins, and especially window functions until "top earner per department" and "running total per customer" are automatic.
Rung includes a live in-browser SQL sandbox with 19 problems from easy GROUP BYs to Hard window-function and correlated-subquery questions, each checked against a reference result.
Try one now, no account
Error Rate by Service from Raw Logs
Each log line should have the form "service level message" (three or more space-separated parts, where `level` is INFO, WARN, or ERROR). Given a list...
Solve it in your browser →The editor and test runner load right in the page. This is the format FDE coding rounds use.
Practice FDE SQL in a live sandbox
Practice FDE SQL in a live sandbox →Frequently asked questions
What SQL is tested in a Forward Deployed Engineer interview?
GROUP BY and HAVING, multi-table joins, subqueries, CTEs, and, most importantly, window functions (ROW_NUMBER, RANK, SUM/AVG OVER). Questions are usually multi-step transformations against a realistic schema rather than single-line trivia.
Do FDE interviews test window functions?
Yes, window functions are one of the strongest separators in the SQL round. Expect "top N per group", running totals, and ranking questions that cannot be answered cleanly with GROUP BY alone.
How do I prepare for a SQL interview quickly?
Practice writing and running real queries, not memorizing syntax. Focus on join grain (to avoid double-counting), window functions, and multi-step CTEs. A live SQL sandbox that checks your answer against a reference result builds the right instincts fastest.
How is the FDE SQL round different from a data-analyst SQL screen?
The question shapes overlap, but FDE rounds lean harder on multi-step transformations against messy, realistic schemas and on reasoning about correctness out loud (grain, NULLs, double-counting), because on the job you write this live in a customer's database, not against a clean interview table.