Geospatial Data Engineer
$111,000–$144,000 year
Remote · San Francisco, California, United States
Job Summary
Geospatial Data Engineer on the Geospatial Analytics team responsible for designing, building, and maintaining data infrastructure powering geospatial analytics—from ingestion pipelines and spatial databases to automated processing systems and internal tooling. Collaborate with geospatial analysts, data scientists, and cross-functional partners to ensure data reliability, scalability, and accessibility. Bring 2–4 years of data or software engineering experience with Python and SQL, and hands-on familiarity with geospatial data formats and PostGIS. Write clean, tested code, participate in code reviews, and help establish engineering standards while contributing to the full data lifecycle.
Required Qualifications
- Bachelor’s degree in Computer Science, Engineering, Geography, Statistics, Math, or a related field
- 2–4 years of experience in data engineering, software engineering, or a closely related role
- Proficiency in Python, including experience writing modular, tested, and maintainable code using geospatial Python libraries such as GeoPandas, Shapely, Rasterio, or GDAL
- Solid SQL skills and hands-on experience with PostgreSQL and PostGIS
- Fluency with Git/GitHub workflows, including branching strategies, pull requests, and code reviews
- Working knowledge of geospatial data formats (GeoJSON, GeoTIFF, Shapefile, etc.) and coordinate reference systems
- Experience building or maintaining ETL or data pipeline workflows in a production environment
- Strong communication skills and ability to work collaboratively across technical and non-technical stakeholders
- Experience with ArcGIS Pro or QGIS highly preferred
- Nice to have: Cloud-based geospatial platforms/data warehouses (BigQuery, Snowflake, AWS, GCP); Salesforce integrations; ArcGIS Online/Enterprise; Temporal, Airflow, Prefect; integration with internal dashboards/APIs
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