Geospatial Analytics Engineer

Build and develop a containerized geospatial analytics platform combining an interactive web UI, a Python analytics API backed by an analytical database (DuckDB), and raster tile services for Cloud Optimized GeoTIFF (COG) datasets.You will work closely with data engineers and full-stack developers to develop our geospatial system end-to-end: data pipelines, geospatial correctness, performance, and user-facing workflows.

Responsibilities:

• Deliver features end-to-end across UI, APIs, and geospatial services.
• Design and optimize analytical schemas and SQL (DuckDB) for performance.
• Implement real-time query and data delivery via WebSockets/streaming (Arrow/JSON) plus REST endpoints for metadata and access.
• Build interactive, map-centric dashboards (choropleths, markers, filters) with correct geospatial interpretation and responsive UI.
• Build coordinated, interactive analytics so actions in one view (map, chart, or layer) update all others.
• Improve performance and reliability through profiling, caching, defensive data handling, and sane defaults.
• Strengthen developer experience via reproducible Docker workflows and robust automated testing.

Essential Qualifications:

• Strong TypeScript + React experience building map-driven SPAs.
• Strong SQL and analytical mindset; familiarity with columnar/Arrow-style data exchange.
• Experience building reactive data architectures with centralized state coordination (e.g., Mosaic, Observable Plot, or similar frameworks).
• Expertise in geospatial query optimization across both vector (DuckDB/PostGIS) and raster (COG/TiTiler) data sources.
• Practical experience with COGs and raster tooling (e.g., rasterio, rio-tiler, TiTiler-like systems).
• Comfort with containerized, multi-service development and E2E testing (Playwright or equivalent).

Bonus Qualifications:

• AWS data delivery patterns (S3, IAM, CDN/CloudFront) and cost and performance trade-offs.
• Advanced geospatial concepts (STAC, PostGIS, vector tiles, raster statistics).
• Observability for multi-service systems (logging, metrics, tracing).
• Familiarity with LLM-assisted data tooling or agent-style workflows, with safety and evaluation in mind.

Desired Personal Attributes:

• Start-up mentality (flexible & pragmatic)
• Strong collaboration skills in multi-disciplinary environments.
• Systems thinking and a bias toward reliability & scalability.
• Ability to design practical, maintainable geospatial & analytics infrastructure.

What We Offer:

• A mission-driven environment focused on improving global public health.
• Opportunities to work on high-impact problems in AI and pathogen genomics.
• A flexible and collaborative work culture with a commitment to innovation.
• Low bureaucracy with direct ownership of the product — no ticket-factory work.
• Competitive salary and paid leave.
• Hybrid-friendly work pattern.

Join us to help operationalize the future of genomic intelligence and deploy real-world AI tools that matter.