Geospatial Software Engineer

Job Specification: Geospatial Software Engineer (Infrastructure & AI Routing) Role Overview We are an elite, high-margin UK power engineering consultancy specializing in grid connections, renewable energy infrastructure, and hyperscale data centre projects (up to 132kV/400kV). We are seeking a talented Geospatial Software Engineer to design, build, and deploy a proprietary, internal automated route-optimization engine. This platform will ingest live UK environmental and civil data registries (DEFRA, Ordnance Survey, National Grid) to automatically generate optimal, constraint-free linear corridors (cables/overhead lines) before passing clean spatial data to our high-volume AutoCAD production teams in the UK and India. • Position: Remote (India-based or Global) • Type: Full-Time Contract / Permanent • Reporting to: Lead Project Director (UK) Core Responsibilities • Build Automated Data Pipelines (ETL): Develop automated Python scripts to pull live spatial vector layers via REST APIs from UK platforms, including the DEFRA Data Services Platform (Ancient Woodland, SSSI, Flood Zones) and the Ordnance Survey (OS) Data Hub (MasterMap Topography, Highways, Railways). • Develop Routing Algorithms: Implement and customize least-cost path and network optimization algorithms (e.g., Dijkstra's, A*, or genetic algorithms using Python libraries like NetworkX, GeoPandas, and Shapely) to auto-route 132kV underground cable lines around high-penalty constraints. • AutoCAD Pipeline Integration: Create automation scripts that translate optimized GIS routes and vector coordinates directly into clean DXF/DWG formats, allowing our AutoCAD teams to immediately begin detailed engineering design without manual tracing. • Automated Risk Reporting: Design a backend function that automatically cross-references generated routes against known boundaries to export an instant Red-Amber-Green (RAG) Risk & Crossings Report in PDF/Excel format. Required Technical Skills • Expert-Level Python: Mastery of Python for geospatial processing and data manipulation. • Geospatial Tool Stack: Extensive experience with open-source GIS libraries: GeoPandas, Shapely, PyQGIS, Fiona, GDAL/OGR, and NetworkX. • API Integration: Proven experience connecting to, parsing, and managing high-volume REST APIs, WFS (Web Feature Services), and WMS (Web Map Services). • CAD Automation: Experience with Python-based CAD automation tools (e.g., ezdxf or AutoCAD Python APIs) to bridge the gap between GIS data and drafting environments. [1] • Database Management: Competency with spatial databases, specifically PostgreSQL / PostGIS.