Geospatial Data Engineer

Role overview

Help build the geospatial backbone of a data intelligence platform by turning raw satellite and spatial sources into trustworthy, analysis-ready assets. The work blends backend engineering with Earth observation know-how, spanning ingestion, transformation, cataloging, and delivery at production scale. You'll collaborate closely with platform engineers and scientific users to make imagery and vector data genuinely useful.

• Wire up provider integrations for imagery search, acquisition, download, and delivery tracking, handling authentication, rate limits, retries, and inconsistent vendor metadata.
• Build raster ETL pipelines covering metadata extraction, reprojection, resampling, mosaicking, cloud-optimized outputs, overviews, and quality checks across optical, multispectral, thermal, and SAR products.
• Design vector pipelines for geometry validation, schema normalization, spatial partitioning, indexing, and the production of efficient columnar geospatial assets.
• Implement imagery cataloging using STAC-compatible tooling and spatial data services on PostGIS, preserving footprints, acquisition metadata, lineage, and access controls.
• Engineer storage and delivery paths over S3-compatible object stores with range requests, caching, and tiling strategies suited to large rasters.
• Monitor freshness, completeness, lineage, and cost, and diagnose malformed deliveries, catalog inconsistencies, and processing bottlenecks.

• Several years of data or backend engineering experience with substantial hands‑on work on geospatial or Earth observation data.
• Strong Python skills plus practical Go experience for services, integrations, or processing infrastructure.
• Working knowledge of raster tooling (GDAL, Rasterio) and vector libraries such as Geo Pandas or Shapely.
• Solid SQL and PostgreSQL/PostGIS expertise, including spatial indexing, query planning, and bulk loading.
• Familiarity with GeoTIFF, GeoJSON, Geo Package, and Shapefile, with sound understanding of coordinate reference systems, nodata handling, and geometry validity.
• Experience running containerized services on Linux with automated tests and monitoring; eligibility to work in Germany and clear English communication.

• STAC, pgSTAC, TiTiler, Martin, or OGC data services.
• Distributed geospatial frameworks such as Spark, Dask, xarray, DuckDB, Arrow, or Apache Sedona, plus multidimensional formats like Zarr, NetCDF, and HDF
  5.
• Analytical table formats such as Iceberg, workflow engines like Temporal, Kafka, Kubernetes, and self-hosted or air‑gapped operations.
• Provider delivery formats such as DIMAP, satellite acquisition APIs, or Copernicus Sentinel-1/Sentinel-2 product handling.
• Background supporting scientific processing in production, work in defense or intelligence contexts, or German language skills.

• Ownership of the pipelines and data services behind a growing geospatial platform.
• Exposure to diverse satellite and spatial datasets, from raw provider deliveries to reusable analytical products.
• Python and Go environment with room to shape processing, cataloging, and performance practices.
• Remote-first arrangement in Germany with regular team sessions in Berlin and occasional meetups in Frankfurt and Munich.
• 30 days of vacation, equipment and learning support, and close collaboration across geospatial, platform, and applied science teams.