Data 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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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

Schedule

Shift type

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Weekend work

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Company

Company stage

Seed From the posting source checked Jun 20, 2026

Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Data Engineer San Francisco THE ROLE We're seeking a Data Engineer to transform large-scale geospatial datasets into structured, reliable, and accessible formats that power Mach9's ML and product pipelines. You'll work with high-volume data sources - laser scan point clouds, imagery, and a long tail of geospatial formats - and own the systems that get them ingested, standardized, stored, and made available for training, perception, and production use in a consistent and efficient way. This role sits at the front of everything we do: our models are only as good as the data feeding them, and you'll be the one making that data trustworthy at scale. RESPONSIBILITIES - Develop and maintain scalable, reproducible workflows for ingesting and processing large volumes of point cloud, imagery, and geospatial data. - Convert datasets from various sensor providers into Mach9's standardized internal formats. - Build CI/CD pipelines and automated checks that guarantee the correctness and consistency of data pipelines, including regression detection on dataset processing. - Optimize processing performance, query speed, and storage efficiency across large geospatial datasets. - Work closely with the customer success team to efficiently resolve issues and unblock customer projects. - Build and maintain agentic harness for automated dataset triage and code patching. Automatically propose or apply fixes, and escalate when human judgment is needed. - Work closely with ML and product teams to make data readily usable for training, inference and visualization. - Work closely with customers and data-provider partners to facilitate data integration (with occasional travels). - Puzzle-hunting:

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