Geospatial Data Engineer

orcristtechnologies; United States; Remote; USD 120,000 - 180,000; Full time

Benefits offered by this job

Remote-first arrangement

40 days of paid leave

Equipment and learning support

Job summary

orcristtechnologies is seeking a backend engineer to help build the geospatial backbone of a data intelligence platform. You will integrate imagery providers, design raster and vector pipelines, and ensure scalable storage and delivery of large geospatial assets.

Responsibilities include STAC cataloging, PostGIS-based workflows, and containerized services on Linux, with emphasis on Python and Go, GIS formats, and Earth observation expertise.

Qualifications

• 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 GeoPandas or Shapely.
• Solid SQL and PostgreSQL/PostGIS expertise, including spatial indexing, query planning, and bulk loading.
• Familiarity with GeoTIFF, GeoJSON, GeoPackage, 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.

Responsibilities

• 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.

Skills

Python

Go

SQL

Tools

GDAL

Rasterio

GeoPandas

Shapely

PostGIS

Job description

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.

Responsibilities

• 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.

Requirements

• 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 GeoPandas or Shapely.
• Solid SQL and PostgreSQL/PostGIS expertise, including spatial indexing, query planning, and bulk loading.
• Familiarity with GeoTIFF, GeoJSON, GeoPackage, 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.

Nice to have

• 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 HDF5.
• 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.

Benefits and work setup

• 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.