Data Platform Engineer

  • Location: Ontario
  • Type: Direct Hire
  • Job #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.
 

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