Geospatial Data Engineer

We are seeking a Geospatial Data Engineer to design, build, and scale geospatial data products that support Digital Agriculture Solutions, Crop intelligence platforms.

The ideal candidate combines expertise in remote sensing, geospatial processing, and cloud-native architectures to transform Earth observation data into scalable, production-ready analytics and machine learning solutions.

• →Design and implement scalable geospatial processing workflows using Earth observation datasets (e.g., Sentinel, SAR etc.) to generate field/regional scale insights for digital agriculture applications.
• →Architect, develop, and deploy scalable geospatial data products and cloud-native processing solutions that integrate multiple data types with earth observational datasets to support production digital agriculture solutions.
• →Partner with data scientists, agronomists, software engineers, and product teams to operationalize geospatial insights and enable scalable solutions.
• →Provide technical leadership in geospatial engineering, remote sensing, cloud architecture, and emerging Earth observation technologies, while promoting best practices in scalability, data quality, and system design.

• Bachelor's or Master's degree in Geospatial Science, Remote Sensing, GIS, Computer Science, Data Engineering, or a related field.
• 5+ years of experience in geospatial engineering, remote sensing, geospatial software development, or related disciplines.
• Strong experience working with raster and vector geospatial data at scale.
• Proficiency in Python and modern geospatial processing frameworks.
• Experience designing cloud-based geospatial processing solutions and production workflows.
• Strong understanding of coordinate systems, projections, raster processing, and spatial analytics.

• Experience with GDAL, Rasterio, GeoPandas, Shapely, Xarray, STAC APIs, or similar geospatial tools.
• Experience with Earth Engine, GCP, AWS, Azure, or distributed processing platforms.
• Experience integrating remote sensing, weather, soil, and observational datasets.
• Knowledge of agricultural analytics, crop monitoring, pest forecasting, disease modeling, or related digital agriculture applications.
• Experience supporting machine learning workflows using geospatial features.

• Deliver scalable geospatial processing systems and high-quality satellite-derived data products.
• Enable production digital agriculture solutions through effective integration of remote sensing, weather, soil, and observational datasets.
• Drive innovation in geospatial analytics and remote sensing capabilities across the organization.