Remote Sensing Data Engineer
Team: Carbon Projects
Location: Remote
Commitment: Full-time
Workplace Type: remote
Salary:
Responsibilities
- Summary: Conduct remote sensing analytics and modeling; develop scalable analytical and reporting tools; manage GIS data collection, storage, and version control across team members; engage in strategic planning & process improvement; provide support to collaborate with Land and Forestry teams
- Manage and analyze large datasets off-line and in cloud computing and storage platforms.
- Analyze large and complex geospatial datasets and remote sensing data (e.g. satellite imagery, LiDAR, SAR, multispectral data, etc.)
- Design and implement novel predictive, statistical, and machine learning models related to forestry, land use, carbon sequestration, biodiversity, conservation planning, and climate resilience.
- Automate statistical and geospatial analysis processes using Python, R, or other programming languages
- Create clear and impactful reporting tools (e.g. maps, webapps, dashboards) to communicate geospatial information and insights
- Facilitate data accessibility using ArcGIS, QGIS, or other proprietary tools for ease of use across team members
- Maintain and update internal geospatial databases, ensuring data quality, consistency, and version control
- Integrate data from Land, Forestry, and Carbon teams to support commercial initiatives
- Ensure high standards of data accuracy and ethical use in all geospatial analyses and models.
- Conduct quality control checks on geospatial datasets
- Provide technical mapping support to other team members as needed.
- Work closely with Land, Forestry, and Carbon teams to uncover new operational insights
- Identify opportunities to improve geospatial workflows and contribute to the development of best practices.
- Support research and development efforts in geospatial analytics and remote sensing applications.
- Assist in the identification and development of new project opportunities
Qualifications
- Education: A MSc or PhD with >2+ years in Data Science, Computer Science, Remote Sensing, GIS, Spatial Ecology, Environmental Science, or a related field
- Programming Skills: Proficiency in Python, R, Java, SQL with experience using geospatial libraries, and machine learning analytics
- GIS & Remote Sensing Expertise: Strong skills in GIS software (ArcGIS, QGIS, Google Earth Engine) and remote sensing data and processing (e.g., Landsat, Sentinel, MODIS, GEDI, etc); Knowledge of geostatistics, predictive modeling, spatiotemporal analysis
- Data Engineering & Cloud Computing: Experience building data pipelines, ETL processes, and spatial databases and working with cloud computing platforms.
- Problem-Solving & Communication: Strong analytical skills and ability to communicate complex findings effectively to diverse audiences.
Pluses
- Familiarity with reforestation, natural climate solutions, carbon markets, and reporting frameworks.
- Experience with big data tools for large-scale geospatial processing.
- Knowledge of forestry, conservation, environmental, and climate sciences.
- Experience developing and managing dynamic databases for remote sensing analyses.
- Proficiency in web apps, dashboards, and visualization tools
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