SQL & Data Engineering is a core part of the Scale AI Forward Deployed Engineer loop. Window functions, gaps-and-islands, Spark internals, lakehouse design, idempotent pipelines and CDC: the Databricks/Snowflake/Palantir data track. Below are the sql & data engineering questions to prepare, the ones tagged to Scale AI first, then the highest-signal questions from our SQL & Data Engineering track, each with an answer written to a senior-engineer bar.
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SQL & Data Engineering questions tagged to Scale AI
More SQL & Data Engineering questions for Scale AI's loop
The highest-signal sql & data engineering questions candidates rate most useful, modeled on what Scale AI's Forward Deployed Engineer loop tests.
Concepts behind Scale AI's SQL & Data Engineering round
The vocabulary and mental models these questions assume. Start with the foundations free; the deeper, interview-defining ideas are part of premium.
FoundationalSQL Window FunctionsWindow functions compute a value across a set of rows related to the current row without collapsing them, so you can rank, compare to a neighbor, or run a cumulative total while keeping every row. They are how analysts answer 'compared to what?' questions in pure SQL, and most interviewers use them to tell people who know SQL from people who know GROUP BY.
CoreSign inIdempotent Data PipelinesPipelines retry, get re-run, and get backfilled, and every one of those re-runs must produce the same result as running once. Idempotency is the property that makes that true: write by key with upsert or partition overwrite, never blind append, so a retry cannot double-count. It is the single property that makes a pipeline safe to operate, because the alternative is a 2 a.m. page where you cannot tell if it is safe to run the job again.
CoreSign inData Quality and ValidationA deployment lives or dies on the customer's data, and that data is worse than their sample suggested. The job is to build automated quality gates (schema, null, range, uniqueness, freshness) at the boundary, quarantine bad records instead of failing the whole batch, and alert on the rate so a Tuesday-shaped degradation surfaces before a dashboard goes wrong. This is the difference between a pipeline that fails loudly and one that lies quietly.
CoreSign inDeduplication and LSHExact duplicates fall out of hashing the normalized content, but near-duplicates (the same record with a typo, a reordered address, boilerplate that repeats across documents) need similarity, not equality. MinHash estimates Jaccard similarity cheaply, and Locality-Sensitive Hashing buckets similar signatures so you only compare likely pairs instead of all O(n^2). This is a constant reflex when merging messy enterprise data and when curating training corpora.
Advanced🔒 PremiumGaps and IslandsGaps and islands is the SQL pattern for collapsing a sequence of rows into the contiguous runs (islands) and the breaks between them (gaps). The trick is a difference of two row numbers that stays constant inside a run, giving every row in the same island an identical group key you can then aggregate. It powers sessionization, login streaks, and contiguous date-range queries, and interviewers love it because the naive self-join answer is both slow and wrong on ties.
Advanced🔒 PremiumChange Data Capture (CDC)Change Data Capture streams row-level inserts, updates, and deletes out of a source database so downstream systems stay in near-real-time sync without full reloads. The strong form reads the database transaction log rather than polling tables, which captures deletes, preserves commit order, and adds almost no load to the source. The hard parts are ordering, tombstones for deletes, and applying the stream idempotently so a replay does not corrupt the target.
Advanced🔒 PremiumETL vs ELTETL transforms data before it lands; ELT loads it raw and transforms inside the warehouse. Cheap elastic compute made ELT the default, but ETL is still correct when data must never land in raw form, which is exactly the situation in most regulated customer environments an FDE deploys into.
Advanced🔒 PremiumQuery Optimization and Execution PlansWhen a customer says a query is slow, the answer lives in the execution plan, not in intuition. Reading a plan tells you whether the engine is scanning when it should seek, exploding rows in a join, or shuffling across a network, and those three causes have nothing in common except the symptom.
Scale AI's Forward Deployed Engineer loop draws sql & data engineering questions such as "The customer says the synced orders table 'does not match' their ERP. Show me the queries you run.", "Find the users who were active in January but not in February.", "Find the customers whose return rate exceeded 30% last quarter.". Window functions, gaps-and-islands, Spark internals, lakehouse design, idempotent pipelines and CDC: the Databricks/Snowflake/Palantir data track. The full set, ordered easy to hard with expert answers, is below.
Work the sql & data engineering questions on this page in order, study the underlying concepts linked below, then review Scale AI's full interview process for how this round fits the loop. For Scale AI specifically, interviewers weight this: Enterprise, government, and defense deployments.
Yes. Scale AI hires Forward Deployed and Senior Forward Deployed AI Engineers who work with enterprise, defense, and government customers to deliver data and GenAI systems.
Expect a recruiter screen, engineer screens, and live coding, plus discussion of GenAI deployment and the data infrastructure behind model development. The process usually runs several rounds. Candidate reports from early 2026 describe a recruiter call followed by a 45-minute live-coding round with an engineer, expanding to three or four rounds and six or seven interviews over roughly three weeks.
Other Scale AI interview rounds
The other tracks Scale AI's Forward Deployed Engineer loop tests.
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