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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in developing and maintaining spatial data frameworks, integrating diverse geospatial datasets, and automating geospatial workflows using Python and SQL. Proficient in GIS platforms and spatial data standards, with a strong understanding of data quality and analytical methodologies for land management applications.
Highest-signal resume keywords
GIS ExpertisePython AutomationSpatial Data IntegrationGeospatial Data ScienceCloud-Based Data Architecture
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
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Hard Skills
Geospatial Data ScienceSpatial AnalysisData Processing WorkflowsCoordinate Reference SystemsGeospatial LibrariesPythonSQLGIS PlatformsSpatial DatabasesEarth Observation
Soft Skills
Analytical ThinkingProblem SolvingCollaborationCommunication
Tools & Technologies
QGISArcGISPostGISGeoPandasGDALRasterioShapelySentinel HubLiDARAPIs
Industry Keywords
Land ManagementEnvironmental ManagementNatural CapitalAgricultureUK Spatial DatasetsData LakesData MeshSpatial Machine LearningGeospatial Web ServicesSpatial Data Licensing
Tech Stack
Tools & technologiesAWSCloudETLPostGISPythonSQL
About the role
Key responsibilities & impact- Develop and maintain Origin’s spatial data framework
- Integrate national and regional datasets covering land use, land cover, field boundaries, land ownership, soils, topography, water, habitats, environmental designations and planning constraints
- Develop spatial rules and analytical approaches for characterising fields, land parcels and landholdings
- Connect land characteristics, opportunities and constraints with relevant products and services across the Group
- Develop repeatable approaches for identifying and screening land opportunities at regional and national scale
- Work with domain specialists on spatial classifications and analytical rules for agricultural, environmental and land-management applications
- Define how spatial data is ingested, represented, stored, catalogued and distributed within the data architecture
- Design automated pipelines for ingesting, transforming, validating, classifying and refreshing geospatial datasets
- Develop reusable spatial data products for applications, analytics, APIs and AI/ML services
- Establish spatial data standards for coordinate systems, identifiers, topology, metadata, lineage and data quality
- Integrate spatial data with customer, operational and commercial datasets
- Ensure geospatial workflows are scalable and reproducible rather than dependent on manual desktop GIS processing
- Work with engineering teams to operationalise GIS prototypes
- Undertake advanced spatial analysis including intersection, proximity, suitability, terrain and hydrological analysis
- Develop automated spatial workflows using Python, SQL and geospatial libraries
- Work with vector, raster and point-cloud/elevation datasets
- Develop methods for assessing spatial data quality, completeness and accuracy
- Support prototyping and visualisation using QGIS where appropriate
- Incorporate Earth Observation-derived information into Land Use Intelligence
- Work with optical and other EO-derived datasets, including Sentinel imagery
- Use EO outputs such as vegetation indices, land-cover classifications and change-detection products
- Define how EO outputs are linked to fields and other spatial entities
- Establish mechanisms for storing and distributing EO-derived outputs through the Data Mesh
Requirements
What you’ll need- Professional experience in GIS, Geospatial Data Science, Geospatial Engineering or a related discipline
- Strong understanding of vector and raster geospatial data
- Experience working with cloud-based data architectures, ideally AWS
- Strong experience with GIS platforms such as QGIS, ArcGIS or equivalent
- Experience working with spatial databases, ideally PostGIS
- Python experience for geospatial automation
- SQL experience
- Experience with geospatial libraries and tools such as GeoPandas, GDAL, Rasterio, Shapely or equivalent
- Experience processing large regional or national spatial datasets
- Experience designing repeatable spatial ETL/data-processing workflows
- Strong understanding of coordinate reference systems, topology, spatial accuracy and data quality
- Experience consuming APIs and geospatial web services
- Ability to translate business requirements into spatial analytical rules
- Working knowledge of Earth Observation and satellite imagery
- Experience within agriculture, environmental management, natural capital or land management
- Experience with UK spatial datasets such as Ordnance Survey, OpenStreetMap, Land Registry, Defra or Environment Agency data
- Familiarity with data lakes, data meshes or modern enterprise data architectures
- Experience with Sentinel-1 and/or Sentinel-2
- Sentinel Hub or comparable EO-platform experience
- Experience with LiDAR and terrain/hydrological analysis
- Familiarity with spatial machine learning or GeoAI
- Experience publishing spatial data through APIs or web applications
- Understanding of spatial data licensing and governance
Benefits
Comp & perks- Hybrid work arrangement
- Full-time employment