This list contains only the countries for which job offers have been published in the selected language (e.g., in the French version, only job offers written in French are displayed, and in the English version, only those in English).
Geospatial Data Engineering: Experience designing and maintaining scalable data pipelines for spatial datasets and geospatial analytics workloads
Python Data Engineering Stack: Strong Python experience using libraries such as Pandas, NumPy, SQLAlchemy, pytest, and other data engineering tools
Geospatial Libraries & Tooling: Hands-on experience with GeoPandas, Rasterio, Xarray, rioxarray, QGIS, or similar spatial processing tools
Spatial Databases: Expertise working with PostgreSQL/PostGIS or other spatially enabled databases for large geospatial datasets
Workflow Orchestration: Experience with pipeline orchestration tools such as Airflow, DBT, or similar data workflow frameworks
Cloud Data Platforms: Experience deploying and managing data pipelines within AWS or comparable cloud infrastructure environments
Containerized Data Workflows: Familiarity with Docker and version control systems (Git) for managing reproducible data engineering environments
Geospatial Data Integration: Experience ingesting and harmonizing multi-source geospatial data (public datasets, sensor data, satellite or environmental datasets)
Data Quality & Validation: Experience implementing data validation, testing, and quality assurance processes within data pipelines
Geospatial Analytics & Visualization: Ability to support spatial analysis, mapping workflows, and geospatial insight generation for technical and non-technical stakeholders