dClimate Labs
Climate Tech
Full-StackGeospatialDataEngineer
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Full-Stack Geospatial Data Engineer at dClimate Labs. Skills: Full-stack development, Geospatial data engineering, Satellite data processing, Cloud infrastructure, DevOps. Architect end-to-end systems. Design satellite-image processing pipelines (STAC → xarray → Parquet/Zarr/IPFS)”
What You'll Achieve.
Turn petabytes of Earth observation imagery into auditable metrics of carbon stocks, vegetation health, and land use change so that climate finance can flow where it matters.; Advance CYCLOPS across the full stack, from APIs and geospatial data pipelines to frontend tools; Own the product & the pipeline; Impact at scale: Each line of code helps move millions of tonnes of CO₂ equivalent through verifiable nature-based projects.
Industry & Context.
Systems thinking: Comfortable reasoning about distributed systems, eventual consistency, and data-versioning at petabyte-plus scale.
What They're Looking For.
Must Have
Fluent across the stack: Python for data and Typescript, React/Next. js, Node. js, Data-infrastructure chops: Dask/DuckDB; S3 & object-store Data pipelining with orchestration tools like Prefect, columnar formats (Parquet, Arrow) and chunked stores (Zarr, Cloud-Optimized GeoTIFF), Docker., GIS / remote-sensing know-how: Google Earth Engine, QGIS, Rasterio, GDAL, PROJ, xarray, GeoPandas, STAC, EO tiling schemes, Cloud & DevOps: Docker, IaC, Prefect, AWS compute services and observability (Prometheus/Grafana, OpenTelemetry)., Systems thinking: Comfortable reasoning about distributed systems, eventual consistency, and data-versioning at petabyte-plus scale., Bias for action & ambiguity tolerance: You turn half-written Notion docs into shipped features without hand-holding., Mission-driven: You want your work to fight climate change.
Nice to Have
Experience leading a small team or owning a large production system., GPU accelerated image processing (cuDF, RAPIDS, TorchGeo)., Machine Learning knowledge and MLOps (PyTorch/TensorFlow), Experience with carbon/MRV methodologies or environmental/agricultural science.
What You'll Do.
Architect end-to-end systems
Design satellite-image processing pipelines (STAC → xarray → Parquet/Zarr/IPFS)
Design microservices that expose results via GraphQL/REST
Ship product features
Build dashboards in Next. js/React
Build geospatial APIs in Node/Python/FastAPI
Scale & harden systems
Automate everything with IaC (Terraform/Pulumi)
and robust orchestration using Prefect
Profile memory & I/O to keep petabyte workflows affordable
Lead & mentor engineers
Establish engineering best practices
How You'll Work.
Team & Collaboration
Work with professors and academics; Play a role in hiring and mentoring subsequent engineers
Full Job Description
ABOUT THE ROLE dClimate Labs https://www.dclimate.ai/ is building EarthOS, an AI-powered climate and geospatial intelligence platform that turns satellite, environmental, and asset-level data into actionable insights for companies, investors, and insurers. CYCLOPS https://www.cyclops.ai/ is dClimate Labs’ natural capital and carbon MRV platform. It helps carbon project developers, investors, buyers, and agricultural companies monitor land cover, vegetation health, carbon stocks, land-use change, and project risks using satellite data and geospatial analytics. Our mission is to turn petabytes of Earth observation imagery into auditable metrics of carbon stocks, vegetation health, and land use change so that climate finance can flow where it matters. We’re looking for a hands-on full-stack data engineer to advance CYCLOPS across the full stack, from APIs and geospatial data pipelines to frontend tools used by carbon project developers, investors, buyers, and agricultural companies. WHY THIS ROLE IS UNIQUE - Own the product & the pipeline: You’ll design everything from ingestion of raw Sentinel/Landsat scenes to the API that powers on-chain carbon registries and dashboards. - Impact at scale: Each line of code helps move millions of tonnes of CO₂ equivalent through verifiable nature-based projects. - Novel technology: No legacy cruft. Pick the right datastores, cloud primitives, and CI/CD flows from day one. - Educational environment: You will work with professors and academics who are top of their fields so you understand the why, while doing the how. - Path to leadership: You’ll play a role in hiring and mentoring subsequent engineers, setting technical direction for years to come. WHAT YOU'LL DO - Architect end-to-end systems: Design satellite-image processing pipelines (STAC → xarray → Parquet/Zarr/IPFS) and the microservices that expose results via GraphQL/REST. - Ship product features: Build dashboards in Next.js/React and geospatial APIs in Node/Python/FastAPI t
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