Job: Geospatial Data/Backend Engineer
You will have the ultimate green-field opportunity to design and build the entire geospatial data pipeline that will power our products. As our first data engineer, you will be the architect of our "ground truth," finding and integrating novel datasets, staying ahead of the latest research, and building the high-performance services that bring our geo-engine to life.
As a key member of our early-stage team, you will own the entire geospatial data stack, from raw data sourcing and processing to the APIs that serve it.
Your primary focus will be on building robust, scalable pipelines to ingest, process, and optimize massive geospatial datasets for our applications
You will also stay on the pulse of the latest geospatial research, experiment with new data sources and algorithms, and collaborate directly with the product team to shape our data strategy.
- Sourcing and integrating foundational datasets from open sources like OpenStreetMap, regional government portals, and commercial providers.
- Designing and building ETL/ELT pipelines to clean, transform, and load geospatial data into our core database.
- Optimizing data for performance, including generating vector tiles and structuring data for fast, low-latency queries.
- Developing and maintaining high-performance APIs in Go to expose our mapping data to our mobile and web applications.
- Researching and implementing cutting-edge techniques in areas like spatial indexing, routing algorithms, and conflation to make our map smarter.
- Managing and scaling our geospatial database infrastructure, likely using PostGIS.
- Collaborating with product and design to understand data requirements and deliver features that delight users.
- A strong portfolio or GitHub profile showcasing relevant data engineering or backend projects.
- Deep experience with geospatial databases, particularly PostGIS, and a strong command of spatial SQL.
- Proficiency in a backend programming language, with a strong preference for Go and Python.
- Experience with data orchestration (Airflow/Dagster) and cloud infrastructure.
- Hands-on experience processing large-scale geospatial datasets (e.g., the full OpenStreetMap planet file).
- Familiarity with geospatial data formats (e.g., GeoJSON, Shapefile, Protobuf) and tooling (GDAL/OGR).