The Interview Loop

Cursor is hiring one of its first Data Platform Engineers, so the loop is built to find out whether you can stand up infrastructure from near-zero, not whether you can grind LeetCode. Hold the whole shape in your head before you prep a single answer.

The loop is one sustained argument: that you can own the Databricks lakehouse, scale ingestion to billions of events a day and turn recurring data pain into platform primitives, all while the platform is still half-built around you. Every stage probes a slice of that claim.

1. 1Recruiter / hiring-manager screen (~30-45 min). Background, why Cursor, why data platform and your motivation for the AI/LLM space.
2. 2Technical phone screens (1-3 rounds, ~60 min each). Live SQL and Python plus applied data problems, sometimes in a real codebase. AI tools are restricted on the first technical screen.
3. 3Take-home / practical exercise (~4-8 hrs). A realistic pipeline, ingestion or data-modeling problem, common for senior data roles.
4. 4Onsite project day (~8 hrs, NY or SF). Build a real data feature or service with limited docs, then present the trade-offs.
5. 5Data platform system-design deep dive. Ingestion, lakehouse, orchestration and observability architecture under explicit constraints.
6. 6Values / culture / “why Cursor” round. Informal, often over a meal for senior candidates: vision alignment, ownership and intensity.

The full span often runs two to three weeks. The onsite project day is the block that decides it, because it mirrors the real day-one work: scope a data problem under ambiguity, ship a demoable slice and defend the trade-offs to the people you'd sit next to.

Thirty to forty-five minutes, light on code and heavy on fit. The screener is sorting for a specific reason you want data platform work at Cursor, genuine pull toward the AI-coding mission and whether you can stomach being early.

This filter catches more strong engineers than they expect. A generic “I want to work on AI” answer dies here, because the role is about building foundational data infrastructure for a company that already runs at enormous scale and that demands a sharper why.

• Why Cursor specifically, not “I'm excited about AI” - name the data platform as the thing you want to own from the ground up.
• Why data platform over adjacent paths: have a real reason you'd rather build ingestion and lakehouse primitives than be a data scientist or analytics engineer.
• A 90-second story of a billions-per-day ingestion or lakehouse system you owned, with one number that lands (events/day, TB scanned, cost cut).
• Comfort with early-stage ambiguity and high intensity, said plainly - these are explicit culture screens, not throwaway questions.

Expect one to three roughly hour-long screens: live SQL and Python plus applied data problems, sometimes inside a real Cursor codebase. The distinctive twist is the AI-tool policy and getting it wrong can sink an otherwise strong screen.

When AI is allowed, the bar is judgment, not delegation. Use it for targeted syntax and lookup; handing the whole problem to a model reads as a weak signal and gets noticed.

-- Latest row per entity from a CDC stream, safe to re-run
WITH ranked AS (
  SELECT
    *,
    ROW_NUMBER() OVER (
      PARTITION BY entity_id
      ORDER BY event_ts DESC, ingest_seq DESC  -- tiebreak avoids nondeterminism
    ) AS rn
  FROM bronze.events
  WHERE event_ts >= :watermark        -- incremental, not full scan
)
SELECT * FROM ranked WHERE rn = 1;

• Strong window functions and CTEs are table stakes - practice them until they're automatic.
• Know idempotent, incremental transforms cold: a re-run must not double-count or corrupt state.
• Be ready to reason about late-arriving and out-of-order events, not just the clean case.

Senior data roles commonly include a four-to-eight-hour take-home and the likely shape maps straight onto the team's work: build or design a pipeline, ingestion path or data model from realistic, deliberately ambiguous requirements.

Use this stage map to decide what evidence belongs in each round. Memorizing the order is the shallow version. For every stage, prepare one artifact, one story and one question that shows how you reason in the role.

Treat it as production code you'd own on day one, not a happy-path script. The reviewers are platform engineers and they read for the instincts that keep data reliable at scale.

1. 1Read the brief for the ambiguity, then state your assumptions. Write them down at the top - ambiguity tolerance is being graded and silent guessing reads as junior.
2. 2Choose a partitioning and incremental-load strategy on purpose. Partition on a real query/ingest dimension, load incrementally with a watermark and say why.
3. 3Make every transform idempotent. A re-run after a failure must not double-count; use merge/upsert semantics, not blind appends.
4. 4Handle schema evolution and bad data. Tolerate an added column, route malformed records to a dead-letter path instead of crashing the job.
5. 5Add data-quality checks and observability hooks. Freshness, row-count and null/distribution checks signal SLA thinking even at small scale.
6. 6Write a README that argues trade-offs and cost. What you cut, what you assumed, what you'd do with more time and the cost implications of your layout.

The signature stage: a roughly eight-hour, paid onsite in NY or SF where you build a real data feature or service with limited documentation, then present it. It explicitly measures product sense, autonomy and system design - not memorized algorithms.

This is the decisive block because it simulates the job directly. You won't be rewarded for a sprawling, half-built grand design; you'll be rewarded for a working, demoable slice with clear trade-offs and for how you reason and take input while building it.

1. 1Orient before you build. Read what docs exist, run something and ask one clarifying question that shows you understood the ambiguity.
2. 2Cut scope to a demoable slice fast. Decide the one thing that proves the feature works end to end and protect it from feature creep.
3. 3Build correct increments with seams. Idempotent transform, a deliberate partition choice and clear boundaries where the orchestration or governance would slot in later.
4. 4Instrument the one thing that matters. A freshness or row-count check or a latency log - enough to show you'd operate this in production.
5. 5Present the trade-offs. Why this storage layout, why this orchestration boundary, what you cut and exactly what you'd harden next.

Map your prep directly to the stages so nothing surprises you. The loop rewards a few repeatable assets - drilled SQL, a rehearsed end-to-end design and three portfolio stories - far more than broad cramming.

Use this stage map to decide what evidence belongs in each round. Memorizing the order is the shallow version. For every stage, prepare one artifact, one story and one question that shows how you reason in the role.

The single highest-impact drill is the live first screen. Practice SQL and Python without leaning on AI so the autocomplete-only round feels normal, not jarring.

1. 1Rehearse the full reference architecture out loud. Ingestion (batch vs streaming, CDC, dead-letter) → lakehouse (medallion, Delta, partitioning, compaction) → orchestration (Dagster assets, sensors, backfills) → observability and serving.
2. 2Run a self-timed 8-hour mock. Pick a realistic data problem, ship a demoable slice and write the trade-off doc - the scoping muscle is what the onsite tests.
3. 3Bring numbers to every design answer. Volume, latency, cost per TB, freshness SLA; quantifying is the senior tell.
4. 4Prep values stories mapped to the behavioral themes. Extreme ownership, comfort with ambiguity and intensity - concrete situations, not adjectives.