Job
Description
Responsibilities Develop reliable and scalable automation workflows using Python to improve efficiency and reduce manual effort across systems and teams. Collaborate with different domain teams to solve complex problems. Create internal tools and libraries that can be reused across projects to improve development speed and reduce duplication. Set up continuous integration and deployment processes, along with unit and integration testing, to ensure stability and faster releases. Identify and fix performance issues in code or data pipelines to improve speed, memory usage, and reliability of the systems. Stay ahead of issues by proactively monitoring logs, setting up alerts, and conducting root cause analysis for failuresstriving for system resilience and self-healing solutions. Required Skills & Qualifications Education Bachelors or Master’s degree in Computer Science, Geospatial Science, Data Science, or related fields. Experience : 0-2 years of experience in Python development, preferably in geospatial applications. Experience with geospatial libraries such as GeoPandas, Rasterio, Fiona, Shapely, and GDAL. Hands-on experience working with geospatial data formats like GeoTIFF, shapefiles, and KML. Programming & Data Handling : Strong proficiency in Python and knowledge of common Python libraries such as Pandas, NumPy, Dask, and Matplotlib. Familiarity with SQL and handling spatial databases like PostGIS or SpatiaLite. Experience in building basic UI and back-end with Flask/Streamlit Automation & Scripting: Ability to automate repetitive tasks such as raster processing, clipping, reprojection, or file conversion using robust and reusable Python scripts. Experience scheduling and managing automation workflows. Environment & Deployment : Proficient in using Linux/Unix terminals, shell scripting, and command-line tools. Experience managing Python environments with venv, conda. Experience with cloud platforms such as AWS, Google Cloud, or Azure for deployment and processing of geospatial data. Knowledge of containerization and deployment technologies like Docker and Kubernetes is a plus. Problem-Solving & Collaboration : Strong problem-solving skills with the ability to translate business and technical requirements into scalable Python solutions. Excellent teamwork and communication skills to collaborate effectively across teams, including data scientists, engineers, and domain experts. Preferred Skills Familiarity with geospatial machine learning and AI techniques Knowledge of geospatial visualization tools such as Folium, Dash, or Mapbox. Experience with GIS software like QGIS, SNAP. Knowledge of CI/CD pipelines for automating workflows.
