Cursor Data Platform Engineer Interview: Questions & How to Prepare

You own the systems that make company-wide data work reliable, secure, and easy to build on. The posting frames you as one of Cursor's first Data Platform Engineers, working hands-on across the data lakehouse architecture to support a fast-growing data team and, in its words, "uniquely data-savvy business stakeholders." You partner with Data, Product, GTM, and AI research teams to turn messy, repeated data needs into durable infrastructure. Cursor is already at enormous scale, but its data platform is still early.

Ingestion here covers two jobs, and the posting lists them in one line: first-party product data, and third-party business systems. Volume is the hard part of the first. The second is other people's schemas and rate limits, and Cursor names GTM among the partner teams, so a fair share of this is probably business plumbing rather than product events. The posting doesn't say which systems, though, so don't guess at them in the room.

The job description states three charter areas and four example projects. Read them as the source of every interview signal below. An interview can only sample what the role requires.

• Own and optimize the raw data layer: improve the performance, reliability, and cost profile of TB-scale first-party data ingestion so downstream analysis, experimentation, and ETL are faster and more trustworthy.
• Scale orchestration for a growing data team: make Dagster and related orchestration infrastructure reliable, observable, and ergonomic for a large base of data scientists, analytics engineers, and adjacent technical users.
• Expand and secure the ways Cursor's agents do data work: enable new entry points and capabilities for agents while keeping security and privacy requirements high.
• Own, operate, and improve Cursor's Databricks and lakehouse infrastructure as data team size and data volume scale.
• Build and optimize ingestion systems for both first-party product data and third-party business systems.
• Ensure observability, alerting, and operational standards across all data infrastructure layers, and evaluate and roll out tooling (BI platforms, catalogs, ingestion tools, reverse ETL) where it solves real stakeholder needs.

Cursor does not publish stages, a question bank, or a rubric for this role, and unlike some Cursor engineering posts this one carries no "Applying" note describing the process. So treat any detailed "the Data Platform loop is X" claim with skepticism. The responsibilities fix what the interview can sample, and that is what to plan around. Expect low-level data platform depth, orchestration you have operated for real users, and honest reasoning about reliability and cost at TB scale.

The standard advice for a posting with named tools is to go learn the tools. That advice is mostly wasted here. Dagster is the one tool the requirements name and the posting files it as a strong plus, while the other requirement bullets ask for low-level stack building and ingestion at volume, which you either have or you don't. A week of Dagster tutorials will not survive a follow-up about backfills you never ran. Spend the week reconstructing numbers from an ingestion path you did own.

That last row is the one to rehearse, since the posting asks for performance, reliability and cost in a single clause and never says which of them wins. One of them gave way, usually. A shorter retention window, a coarser partition, a freshness target you relaxed, a replay path you decided not to build. Name the one that gave and put the number on it. If all three improved at once, be ready to explain what was mistuned before you arrived.

These are question types the responsibilities imply: the kind of prompt each one naturally invites. Prepare a concrete story for each, and where you can, a design you can whiteboard. Expect follow-ups a level deeper than your first answer, because operating experience at scale is what this posting weights.

Of the six, the one about agents doing data work is where I would cap the effort. The posting gives the goal (new entry points for agents, security and privacy kept high) and no mechanism, so anybody who tells you what Cursor runs internally is guessing. You can still defend the shape of an answer. Say how you would scope credentials for a reader that isn't a person, and what you would refuse to expose at that layer, then ask them what exists today.

Preparation for this role is mostly proof, not trivia. What lands hardest is a data platform you have operated yourself: an ingestion path you scaled, an orchestration layer you kept reliable for others, a data bill you cut. That distinction shows up in the follow-up questions, so bring one you actually ran. Work the steps below, and route the reps through daily Cursor use.

The steps run ingestion first and observability fourth, which looks like an importance ranking. It isn't one. Observability and alerting turn up in three separate places in that posting, so by importance they would sit higher. The order here is by what is hardest to fake in a week. Instrumentation you added last month demonstrates fine in a conversation, and volume you never carried does not.

1. 1Build and operate a real ingestion pipeline at meaningful volume: handle schema drift, replay, and backpressure, and measure cost per record. Ingestion scaled to high volume is the requirement the posting states most concretely.
2. 2Run orchestration you own for downstream users (Dagster if you can) with partitions, backfills, and retries, and make failures observable rather than silent.
3. 3Do a lakehouse pass: reason about table formats, partitioning, and query cost on Databricks or a comparable stack, and be ready to defend a build-vs-buy call.
4. 4Instrument observability, alerting, and a simple cost-attribution view so you can defend one concrete cost-versus-reliability tradeoff.
5. 5Work in Cursor daily on a real data repo (SQL, Python, dbt, Dagster or Terraform) using Tab, inline edit, and Agent. Know what changed in Cursor in 2026 so you can speak to the product you'd help run.
6. 6If you're newer to Cursor, ground yourself in the fundamentals first, then run structured reps.

Team size changes what you have to prove. Out of a small data team you have operated all of it, and the gap is the word large in that orchestration requirement, so name the users you actually had, even if there were nine. Out of a big platform org you probably have the volume, so be straight about whether you owned the storage layer or consumed someone else's.

For structured reps, the free Data Platform Engineer practice track works through the role charter, lakehouse ingestion, Dagster orchestration, observability, cost controls, a why-Cursor module, and a mock self-exam. It's built around this job description. It mirrors the topics the posting names, not Cursor's actual questions or rubric.

The fit criteria in the posting are specific, and they double as your prep checklist. The weighting, at least as I read it, is toward low-level platform depth and operating experience over years alone, and one item (Dagster) is flagged as a strong plus rather than a hard requirement.

• 4+ years of full-time data platform engineering experience.
• Built up modern data stacks at a low level, not just written jobs on top of them.
• Scaled performant ingestion of billions of records per day.
• The go-to person for data pipeline orchestration infrastructure used by large user bases.
• A strong plus: Dagster experience.

Read the emphasis: the JD repeats "at a low level" and asks for ingestion you have "scaled," not just designed. Weight your prep toward the foundations underneath, not the jobs on top.

Whether the 4+ years line is hard, I don't know. The posting doesn't say. The other three bullets are about depth and scale, so that is where the prep time goes.