Senior Geospatial Data & GIS Scientist

The Senior Geospatial Data & GIS Scientist provides expertise in geospatial technologies, spatial data management, cloud-based GIS solutions, and geospatial analytics. The role supports the design, implementation, integration, and optimization of geospatial data platforms and services while ensuring data quality, interoperability, governance, and compliance with relevant standards. What would make you a fit for the role: Design, develop, and maintain geospatial solutions using GIS platforms, spatial databases, and open-source geospatial technologies. Perform geospatial data processing, analysis, transformation, and automation using Python and spatial analytics tools. Develop and optimize spatial databases, data models, and geospatial data integration workflows. Implement and support cloud-based geospatial processing and analytics solutions in Azure, AWS, or Google Cloud environments. Contribute to the integration of geospatial data within data lake, lakehouse, and enterprise analytics architectures. Ensure adherence to metadata standards, interoperability requirements, data governance principles, and open data best practices. Assess technical feasibility, risks, dependencies, and solution options, providing recommendations to stakeholders and project teams. Collaborate with multidisciplinary teams and communicate technical concepts, findings, and recommendations to both technical and non-technical audiences. Qualifications What would make you a fit for the role: Bachelor's degree in Geography, GIS, Geoinformatics, Computer Science, Data Science, Engineering, or a related field. Proven experience with GIS desktop, server, and open-source geospatial technologies such as ArcGIS, QGIS, PostGIS, GeoServer, GDAL/OGR, or equivalent. Strong Python programming skills for geospatial processing using libraries such as GeoPandas, Rasterio, Shapely, Fiona, PyProj, Xarray, or similar. Experience with SQL and spatial SQL, including spatial database technologies such as PostGIS, SQL Server Spatial, or equivalent. Hands-on experience with cloud-based geospatial processing and analytics solutions in Azure, AWS, or Google Cloud environments. Knowledge of data lake/lakehouse architectures, geospatial data integration, metadata standards (ISO 19115, DCAT/DCAT-AP, Dublin Core), and interoperability principles. Strong analytical and problem-solving skills, with attention to data quality, reproducibility, documentation, and operational maintainability. Excellent communication and stakeholder management skills in English Ability to work independently and collaboratively in multidisciplinary environments Understanding of SDMX, environmental indicators, or statistical-geospatial integration is considered an asset.