Data Platform Engineer

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Verification

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Salary

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401(k) match

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Work mode

Remote From the posting source checked Jun 20, 2026

In-office days

0 days From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Application

Cover letter

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Assessment

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Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

Data Platform Engineer San Francisco, CA, United States, 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,

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