Geospatial Data Engineer for Satellite Imagery

Job Title: Freelance Geospatial Data Engineer (Satellite Imagery, GEE Automation & VPRM) Job Overview. We are seeking an experienced Freelance Geospatial Data Engineer to build, automate, and maintain data pipelines for satellite imagery and biospheric carbon flux models. In this role, you will focus heavily on automating Google Earth Engine (GEE) workflows to orchestrate environmental data pipelines supporting the Vegetation Photosynthesis and Respiration Model (VPRM). You will be responsible for programmatically extracting remote sensing indices across both legacy/coarse-resolution data (MODIS) and modern high-resolution streams (Sentinel-2), writing optimized outputs directly to Cloud Storage Buckets for downstream modeling. Key Responsibilities Multi-Satellite Pipeline Integration: Build unified data pipelines to ingest, normalize, and fuse MODIS (500m) and Sentinel-2 (10m/20m) surface reflectance datasets. Automate GEE Workflows: Design and deploy automated scripts using the GEE Python API (ee) to schedule satellite data ingestion, cloud masking, and continuous time-series extraction for indices like EVI and LSWI. Cloud Storage Optimization: Manage and pipe processed raster assets from GEE directly into Cloud Object Storage buckets (AWS S3 / Google Cloud Storage), formatting files into cloud-native formats like Cloud-Optimized GeoTIFFs (COGs), Parquet, or Zarr.VPRM Pipeline Execution: Orchestrate end-to-end pipelines that extract, transform, and load (ETL) data from cloud buckets into the VPRM environment for Net Ecosystem Exchange (NEE) calculations. Core Required Skills Prerequisite Terrestrial Biosphere Modeling: Strong theoretical and applied understanding of the Vegetation Photosynthesis and Respiration Model (VPRM). Deep familiarity with the pyVPRM framework or legacy MODIS-based VPRM preprocessors is required. Google Earth Engine (GEE) Automation: Advanced proficiency with the GEE Python API, managing GEE Assets, and handling long-running, scheduled GEE Export tasks to cloud buckets programmatically. Cloud Storage Infrastructure: Hands-on experience optimizing big geospatial data for cloud buckets, including bucket permissions (IAM), lifecycle policies, and prefix partitioning for fast parallel read/writes. Workflow Orchestration: Experience using tools like Apache Airflow, Prefect, or Google Cloud Composer to trigger and monitor complex spatial modeling tasks. Geospatial Python Ecosystem: Expert command of Python libraries including Rasterio, GeoPandas, Shapely, Fiona, and GDAL/OGR.

Preferred Qualifications

Experience using high-resolution land cover products (e.g., ESA WorldCover or Copernicus Global Land Service) alongside Sentinel-2 to resolve micro-vegetation or urban carbon fluxes. Experience with atmospheric transport or inverse modeling frameworks that ingest biospheric carbon fluxes.