Data Platform Engineer

Data Platform Engineer

Engineering · Full-time · San Francisco; New York; Remote

Our mission is to automate coding. The first step in our journey is to build the best tool for professional programmers, using a combination of inventive research, design, and engineering. Our organization is very flat, and our team is small and talent dense. We particularly like people who are truth-seeking, passionate, and creative. We enjoy spirited debate, crazy ideas, and shipping code.

About the role

Take data foundations from 0→1 on the bleeding edge of AI data.

As one of Cursor's first Data Platform Engineers, you'll own the systems that make company-wide data work reliable, secure, and easy to build on. You'll work hands-on across our data lakehouse architecture to support a fast-growing data team and uniquely data-savvy business stakeholders. You'll partner with Data, Product, GTM, and AI research teams to turn messy, repeated data needs into durable infrastructure.

Cursor is already operating at enormous scale, but our data platform is still early. This role is for someone who wants to own the low-level foundations: optimizing TB-scale ingestion, improving resource usage and alerting, codifying access control with infra-as-code, and making pragmatic build-vs-buy decisions across the modern data stack.

Example projects

• 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 agentic data capabilities: Enable new entrypoints and capabilities for agents to do data work, all while keeping security and privacy requirements high.

What you'll do

• Own, operate, and improve Cursor's Databricks and lakehouse infrastructure as the data team size and data volume scales.
• Build and optimize ingestion systems for first-party product data and 3rd-party business systems.
• Ensure observability, alerting, and operational standards across all data infrastructure layers.
• Evaluate and roll out data tooling where it solves real stakeholder needs, including BI platforms, catalogs, ingestion tools, and reverse ETL systems.
• Partner with technical and non-technical partners to understand recurring data problems and turn them into scalable platform solutions.

You may be a fit if

• You have 4+ years of full-time data platform engineering experience.
• You have built up modern data stacks at a low level, not just written jobs on top of it.
• You have scaled performant ingestion of billions of data per day.
• You're the go-to person for data pipeline orchestration infrastructure used by large user bases; Dagster experience is a strong plus.
• You want to build foundational systems at a company where data-savvy users will immediately push them to their limits.