Data Platform Engineer (34735)

· 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.