As the Senior Geospatial Engineer, you will be the primary engineering resource within our Data Operations team, responsible for ensuring the integrity, scalability, and quality of Earth Observation data products. Your work will span the entire pipeline—from raw satellite data acquisition to the delivery of processed geospatial products—focusing on Synthetic Aperture Radar (SAR), Multispectral Imagery (MSI), and SyncFused data.Key ResponsibilitiesGeospatial Data Pipeline Development & Product Engineering
- Build, maintain, and optimize robust data processing pipelines for SAR, MSI, and SyncFused sensor data.
- Automate core processing steps including data ingestion, radiometric corrections, noise reduction, precise orthorectification, and calibration checks.
- Develop specialized pipeline modules for SyncFused products by integrating structural SAR and spectral MSI data.
- Design and implement application-specific workflows (e.g., ship detection, forest inventory) by chaining modular processing components such as speckle filters and atmospheric corrections.
- Ensure pipelines produce reproducible, high-quality deliverables tailored to customer needs.
Algorithm Interaction & Image Quality Control
- Collaborate closely with the Algorithms Team to translate research algorithms into production-ready software.
- Analyze the impact of algorithm changes (e.g., SAR focusing, atmospheric correction for MSI) on output quality metrics like noise, spectral accuracy, and geometric precision.
- Maintain rigorous quality control to ensure only validated algorithms are integrated into production pipelines.
Standards, Calibration & Validation
- Implement comprehensive Data Quality Assessment (DQA) procedures at every processing stage.
- Perform calibration procedures to guarantee that raw sensor data accurately reflects physical measurements (e.g., backscatter for SAR, reflectance for MSI).
- Validate final products against reference datasets and benchmark metrics (e.g., geometric RMSE, radiometric stability).
- Enforce adherence to international standards (e.g., CEOS, ISO) concerning data formats, quality reporting, and metadata management.
Software Development & Engineering Best Practices
- Architect, develop, and maintain clean, modular, and reusable Python components using key geospatial libraries such as GDAL, Rasterio, and GeoPandas.
- Ensure code is well-tested, documented, and scalable for production environments.
Requirements
- Experience: 2+ years in software/data engineering with strong emphasis on geospatial data or remote sensing.
- Sensor Knowledge: Solid understanding of SAR and MSI sensors, including physical principles and sensor-specific geometric effects (layover, foreshortening).
- Programming: Proficient in Python and geospatial libraries including GDAL, Rasterio, GeoPandas, and Shapely.
- GIS Expertise: Deep knowledge of GIS concepts, map projections, and coordinate reference systems (CRS).
- Data Infrastructure: Experience with spatial databases (e.g., PostgreSQL/PostGIS) and scalable geospatial data processing solutions.
Nice-to-Have Skills
- Specific experience with SAR or MSI data processing algorithms and formats.
- Familiarity with cloud-native geospatial data formats such as Cloud-Optimized GeoTIFF (COG) and SpatioTemporal Asset Catalogs (STAC).
- Experience integrating Machine Learning or Deep Learning models into geospatial data pipelines.
Benefits
- Competitive salary and performance bonuses
- Health insurance and wellness programs
- Flexible work arrangements
- Opportunity to work on cutting-edge space and mission-control systems
- Collaborative and innovative work environment
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