Geospatial Data Engineer

Shearwater Aerospace

4d•Hybrid

About The Position

We are building autonomous flight intelligence drones that can make intelligent decisions onboard, adapting to weather, obstacles, and airspace in real-time without human intervention. We are developing weather-aware flight optimization to increase mission success by extending range, endurance, safety, and reliability. The role involves architecting the geospatial brain that makes this possible. This is the first geospatial hire, responsible for building the data processing systems that enable autonomous flight decisions. Working with the CTO, the individual will architect how the systems evolve from initial implementation to production-grade infrastructure that handles real-world operational demands.

Requirements

Core GIS competency: 4+ years building geospatial software (or 3 years if you've shipped impressive systems)
Strong knowledge of common GIS tools and libraries (QGIS, GDAL/OGR)
Experience with geospatial algorithms (visibility analysis, spatial operations, terrain analysis)
Deep understanding of coordinate systems, projections, and spatial data structures
Python ecosystem: Strong Python with scientific computing stack (NumPy, Pandas, SciPy)
Geospatial libraries: GeoPandas, Rasterio, Xarray, Dask
Data infrastructure: Experience with spatial databases (PostGIS, Apache Sedona, etc)
Comfortable with cloud infrastructure (GCP preferred), Docker, Git
Ability to set up automated data ingestion workflows
Comfortable with ambiguity; ask clarifying questions, propose solutions, and deliver incrementally
Communicate clearly; explain technical trade-offs to non-engineers and translate vague product needs into concrete implementation plans
Ego doesn't enter the room when someone questions your approach

Nice To Haves

C/C++ for performance-critical processing
Meteorology, atmospheric science, or aviation background
Degree in GIS, Computer Science, Engineering, or related field. Master's is a plus, but we'll prioritize what you've built over credentials.
Experience with meteorological data (NetCDF, GRIB) or atmospheric models

Responsibilities

Design geospatial processing pipelines that balance performance, accuracy, and extensibility
Build GIS analysis algorithms for weather-aware route optimization
Integrate multi-source datasets (elevation, obstacles, airspace, meteorological models)
Create APIs that enable real-time flight decision-making
Establish patterns and tooling that evolve as our platform matures