Senior Geospatial Software Engineer

4 days ago

Kota, Rajasthan, India beBeePython Full time ₹ 2,50,000 - ₹ 4,00,000
Python GIS Developer Opportunity

This role involves leveraging Python and GIS technologies to design, develop, and maintain scalable geospatial web applications.

Key Responsibilities:
  • Develop and maintain GIS-based web applications using Python frameworks like Django or Flask.
  • Implement interactive, dynamic GIS maps using libraries such as GeoPandas, Shapely, and Folium.
  • Integrate geospatial data with databases (e.g., PostgreSQL/PostGIS, MySQL, or MongoDB) and RESTful APIs.
  • Automate geospatial data processing workflows using Python libraries like ArcPy, Fiona, or PyQGIS.
  • Design responsive front-end interfaces using HTML, CSS, JavaScript, and frameworks like React, Angular, or Vue.js.
  • Collaborate with data scientists and GIS analysts to productionize geospatial models and visualizations.
  • Optimize applications for performance, scalability, and reliability.
  • Troubleshoot and debug issues across the full stack, from front-end interfaces to back-end data pipelines.
Requirements:
  • Bachelor's degree in Computer Science, Geographic Information Systems, or a related field (or equivalent experience).
  • Proven experience as a Full Stack Developer with strong proficiency in Python.
  • Expertise in GIS technologies and Python libraries such as GeoPandas, Shapely, Fiona, ArcPy, or PyQGIS.
  • Experience with front-end technologies including HTML, CSS, JavaScript, and frameworks like React, Angular, or Vue.js.
  • Familiarity with database technologies, particularly PostgreSQL with PostGIS for geospatial data.
  • Knowledge of RESTful APIs, web services, and data integration from various sources.
  • Proficiency with version control systems, particularly Git.
  • Strong problem-solving skills and attention to detail.
  • Excellent communication and teamwork abilities.
Preferred Skills:
  • Experience with cloud platforms like Google Cloud Platform (GCP), AWS, or Azure for deploying GIS applications.
  • Familiarity with ESRI ArcGIS Suite (ArcPro, ArcMap) or QGIS for geospatial analysis.
  • Knowledge of MLOps practices for integrating machine learning models with GIS applications.
  • Experience with containerization (e.g., Docker) and orchestration (e.g., Kubernetes).
  • Understanding of geospatial data formats (e.g., GeoJSON, Shapefiles, KML) and spatial analysis techniques.

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