Data Engineer building data integration pipelines for data lakes and warehouses. Collaborating with stakeholders to meet business requirements in a leading publishing company.
About the role
- Geospatial Data Engineer at GHGSat integrating geospatial data and optimizing AI/ML pipelines. Supporting climate impact mission through data systems and analytics in a hybrid role.
Responsibilities
- Design, implement, and optimise scalable geospatial data and AI/ML pipelines.
- Integrate new data sources, including satellite and terrestrial, both public and proprietary.
- Re-engineer and validate existing pipelines, ensuring high-quality and performance standards.
- Blend and process various geospatial data sources to create artifacts for exploratory analysis and insights.
- Build scripts and automations for geospatial data processing, using tools like QGIS, GeoPandas, Rasterio, Xarray and rioxarray.
- Conduct geospatial analysis and contribute to mapping and visualization.
- Data testing and quality control of geospatial datasets.
- Contribute to the automation of testing, deployment, and monitoring of data pipelines and AI/ML models using DBT, Airflow, Docker, and AWS services.
- Work collaboratively with the Analytics team, Subject Matter Experts, and cross-teams to prototype new data solutions.
- Explore applications of AI/ML for geospatial data and integrate emerging technologies where possible.
- Present findings and recommendations to both technical and non-technical stakeholders, fostering a data-driven culture.
- Communicate complex geospatial data insights in a clear, accessible manner to support informed outcomes.
Requirements
- 2-4 years of experience in data engineering, with specific expertise in geospatial data processing and analysis.
- Proficiency in SQL and geospatial databases e.g., PostgresSQL/PostGIS
- Experience with Airflow, DBT, and dashboarding tools such as Grafana
- Proficiency in Python and experience with libraries like Pandas, pytest, NumPy, sqlalchemy.
- Comfortable with cloud infrastructure (AWS preferred), containerization tools (Docker), and version control (Git).
- Experience with geospatial packages such as GeoPandas, Rasterio, and QGIS is beneficial.
- Knowledge of AI/ML concepts applied to geospatial data is a plus.
- Knowledge of ClearML is beneficial.
Benefits
- Competitive salary + stock options for all full-time employees
- Health/Dental benefits
- Paid Time Off + floating statutory holidays
- Flexible work environment
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