Python GIS Engineer — Geospatial Data Pipelines for an RF Coverage Tool

I need a strong Python geospatial engineer to build and harden data-ingestion pipelines that pull, process, and validate open geospatial datasets. The work is well-defined, back-end, and data-focused — you won't need any domain context to do it well. What you'll do (scope flexible — tell me your strengths) Fetch and process open geospatial data: DEMs (e.g. Copernicus GLO-30, USGS 3DEP), OpenStreetMap features via Overpass, land-cover/canopy rasters, Cloud-Optimized GeoTIFFs read over HTTP range requests. Handle coordinate transforms and local projections (WGS84 ↔ local ENU/AEQD), with correct treatment of resolution, nodata, and edge-tile mosaicking. Clean and validate vector/raster inputs (polygon geometry, attribute tags, elevation sampling at arbitrary points). (Bonus) Convert 2D footprints + terrain into 3D meshes (extrusion, triangulation). (Bonus) Web-map visualization in deck.gl (tile layers, 3D tiles, GeoJSON overlays). Required skills Strong Python + hands-on geospatial: rasterio/GDAL, shapely, pyproj, numpy. Solid grasp of map projections, CRS transforms, and DEM/raster handling. Experience with open geodata sources (OSM/Overpass, public DEM services, COGs). Clean, tested code (pytest) and clear written communication. Nice to have Web mapping — deck.gl / MapLibre / Mapbox. 3D geometry / meshing. FastAPI, Docker, AWS. To apply Briefly describe a geospatial pipeline you've built (data sources, CRS handling, what tripped you up), and share a code sample or repo. One-line answer: what's your approach to reprojecting a raster DEM and sampling elevation at arbitrary lat/lon points?