Advanced GIS Microservice Development
Budget: $30 – $250 AUD
Senior Python Developer – Containerized Geospatial Processing Library (Azure)
Project Overview:
We are seeking an experienced Senior Python Developer with deep expertise in geospatial data handling (GIS) and Docker containerization. The objective of this project is to build a robust, standalone Python library and execution environment that performs standardized geoprocessing tasks on vector and raster datasets.
The final deliverable must be a highly secure, containerized microservice capable of reading from and writing to Azure Blob Storage, with strict input validation.
Core Tech Stack:
Language: Python 3.11+
Geospatial Libraries: GeoPandas, Rasterio, Shapely, PyProj, Fiona
Validation: Pydantic
Cloud: Azure SDK (Blob Storage, Key Vault, Identity)
Infrastructure: Docker (Alpine or Miniconda base handling GDAL/C++ dependencies)
Scope of Work:
You will be responsible for developing a library of core geospatial functions that can be called programmatically.
Environment Setup & Containerization: * Develop a Dockerfile that cleanly resolves the complex C++ dependencies required by GDAL, GeoPandas, and Rasterio. The container must be optimized for execution speed and memory efficiency.
Base Geoprocessing Functions: * Develop Python functions for standardized spatial operations, including but not limited to:
* Point-to-Polygon generation (bounding boxes, convex hulls).
* Distance buffering.
* Spatial joins and intersections.
* Raster masking and clipping based on vector boundaries.
* Coordinate Reference System (CRS) transformations and alignment.
Strict Input Validation: * Implement Pydantic models for every function. All inputs (geometry types, distances, CRS strings) must be strictly validated before the geoprocessing logic executes to prevent runtime failures.
Azure Integration: * Implement secure I/O functions using the Azure SDK to pull datasets into memory from Azure Blob Storage and push processed results back.
Security Note: Authentication will be handled via Managed Identities; no hardcoded credentials will be permitted.
Out of Scope (What you will NOT be doing):
Front-end web mapping or UI development.
Machine learning or AI integration.
Database administration or architecture.
Deliverables:
A fully documented Python repository (PEP 8 compliant).
A functional and optimized Dockerfile.
A suite of pytest unit tests achieving at least 90% coverage for the geoprocessing functions and Pydantic validators.
A README.md detailing how to build the container and execute the base functions locally and in Azure.
Ideal Candidate Profile:
Demonstrable experience compiling and deploying GDAL/GeoPandas in containerized environments.
Strong understanding of topological errors, CRS handling, and memory management for large spatial files.
Prior experience building microservices within the Azure ecosystem.
Please include examples of past containerized GIS projects or open-source geospatial contributions in your proposal.
Project Overview:
We are seeking an experienced Senior Python Developer with deep expertise in geospatial data handling (GIS) and Docker containerization. The objective of this project is to build a robust, standalone Python library and execution environment that performs standardized geoprocessing tasks on vector and raster datasets.
The final deliverable must be a highly secure, containerized microservice capable of reading from and writing to Azure Blob Storage, with strict input validation.
Core Tech Stack:
Language: Python 3.11+
Geospatial Libraries: GeoPandas, Rasterio, Shapely, PyProj, Fiona
Validation: Pydantic
Cloud: Azure SDK (Blob Storage, Key Vault, Identity)
Infrastructure: Docker (Alpine or Miniconda base handling GDAL/C++ dependencies)
Scope of Work:
You will be responsible for developing a library of core geospatial functions that can be called programmatically.
Environment Setup & Containerization: * Develop a Dockerfile that cleanly resolves the complex C++ dependencies required by GDAL, GeoPandas, and Rasterio. The container must be optimized for execution speed and memory efficiency.
Base Geoprocessing Functions: * Develop Python functions for standardized spatial operations, including but not limited to:
* Point-to-Polygon generation (bounding boxes, convex hulls).
* Distance buffering.
* Spatial joins and intersections.
* Raster masking and clipping based on vector boundaries.
* Coordinate Reference System (CRS) transformations and alignment.
Strict Input Validation: * Implement Pydantic models for every function. All inputs (geometry types, distances, CRS strings) must be strictly validated before the geoprocessing logic executes to prevent runtime failures.
Azure Integration: * Implement secure I/O functions using the Azure SDK to pull datasets into memory from Azure Blob Storage and push processed results back.
Security Note: Authentication will be handled via Managed Identities; no hardcoded credentials will be permitted.
Out of Scope (What you will NOT be doing):
Front-end web mapping or UI development.
Machine learning or AI integration.
Database administration or architecture.
Deliverables:
A fully documented Python repository (PEP 8 compliant).
A functional and optimized Dockerfile.
A suite of pytest unit tests achieving at least 90% coverage for the geoprocessing functions and Pydantic validators.
A README.md detailing how to build the container and execute the base functions locally and in Azure.
Ideal Candidate Profile:
Demonstrable experience compiling and deploying GDAL/GeoPandas in containerized environments.
Strong understanding of topological errors, CRS handling, and memory management for large spatial files.
Prior experience building microservices within the Azure ecosystem.
Please include examples of past containerized GIS projects or open-source geospatial contributions in your proposal.