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GIS Data Engineer
Added: October 4, 2023
Job description About PLENO PLENO is a platform that automates the carbon credit creation process using ML and blockchain technology. We leverage cutting-edge technology and data-driven strategies to provide geospatial-data collection via remote sensors, the most accurate calculation of carbon stocks, and geospatial-data verification system. Profile Description: As a GIS data engineer, you should have proficiency in working with geospatial platforms like Google Earth, NASA's EOSDIS, Esri, and Planet. You must possess an in-depth comprehension of geospatial imagery interpretation and its nuances. It's essential to have advanced capabilities in designing and managing data infrastructure, including data warehouses, lakes, pipelines, and workflow systems. Demonstrated expertise in processing and analysing large-scale geospatial datasets is crucial. Lastly, you should be proficient in Python, particularly tailored for GIS applications and integrations. Responsibilities:
- Development of geospatial data infrastructure for PLENO (https://pleno.earth) on GCP, including spatial data lakes and warehouses, along with the automation of geospatial ETL processes.
- Integration of the geospatial data infrastructure with Google Earth Engine through GCP.
- Real-time geospatial ETL automation which involves:
• Ingesting diverse geospatial data sources, • Harmonising and integrating multiple geospatial datasets into a unified spatial dataset, • Extracting pertinent geospatial insights from the combined dataset, • Preprocessing the extracted data for ML applications. • Rendering geospatial visualisations for the analytical dashboard tailored to user insights. Qualifications: You need to have in-depth knowledge in the following technologies: • Geospatial-Data Platform: Google Earth Engine, NASA EOSDIS, Planet Lab, GFW, Esri, or similar platforms. • Data Tools: Hadoop, Databricks, GCP, Dataflow, etc. • Databases: • Geospatial databases and systems: PostGIS, GeoMesa, ArcGIS • SQL: PostgreSQL • NoSQL: MongoDB • Programming Language: Python with • Apache Sedona • Pandas, GeoPandas • Raster IO • NumPy • Shape • Matplotlib • Plotly • Web Framework: FastAPI and Streamlit. • Testing Framework: PyTest. • Cloud Platforms: Google Cloud Platform (GCP).