GIS Developer career path

• gis-programming-roadmap

  25 533 5.6

  One stop shop for all your GIS Programming needs

This is a useful link that I really like: https://github.com/petedannemann/gis-programming-roadmap/blob/master/README.md

• Fiona

  3 1,124 8.4 Python

  Fiona reads and writes geographic data files

  

As has been said the definition of a GIS dev is far from written in stone, but to chime in from a personal standpoint: most of what I do is data wrangling/analysis with shapely/geopandas for vectors (or pygeos / fiona for performance when data volumes get large, but seeing as Shapely 2.0 just got released one can likely skip this part) + rasterio for rasters as well as parallelising these tasks for performance if needed (ray is great for that) and then performing machine learning learning against the data (mostly with sklearn and torch).

• InfluxDB

  www.influxdata.com sponsored

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• Shapely

  7 3,658 8.5 Python

  Manipulation and analysis of geometric objects

  

As has been said the definition of a GIS dev is far from written in stone, but to chime in from a personal standpoint: most of what I do is data wrangling/analysis with shapely/geopandas for vectors (or pygeos / fiona for performance when data volumes get large, but seeing as Shapely 2.0 just got released one can likely skip this part) + rasterio for rasters as well as parallelising these tasks for performance if needed (ray is great for that) and then performing machine learning learning against the data (mostly with sklearn and torch).

• Pytorch

  334 77,544 10.0 Python

  Tensors and Dynamic neural networks in Python with strong GPU acceleration

  

As has been said the definition of a GIS dev is far from written in stone, but to chime in from a personal standpoint: most of what I do is data wrangling/analysis with shapely/geopandas for vectors (or pygeos / fiona for performance when data volumes get large, but seeing as Shapely 2.0 just got released one can likely skip this part) + rasterio for rasters as well as parallelising these tasks for performance if needed (ray is great for that) and then performing machine learning learning against the data (mostly with sklearn and torch).

• gdal

  44 4,469 10.0 C++

  GDAL is an open source MIT licensed translator library for raster and vector geospatial data formats.

  

Other useful skills would include using CLI utilities (mainly those provided by GDAL, general shell scripting (e.g bash if you're on linux), familiarity with containerisation software (like docker and data visualisation capability either from Python ([matplotlib](https://matplotlib.org/)) or via desktop GIS (which you can obviously already do proficiently).

• fastapi

  462 70,541 9.7 Python

  FastAPI framework, high performance, easy to learn, fast to code, ready for production

If you want to get into web GeoDjango is a popular option for the backend, but you could also learn to roll your own with flask/FastAPI. You also have some choice of JavaScript libraries for the frontend, [Leaflet]() and [OpenLayers]() are likely the most popular frameworks, but there are others (personally I'd recommend OpenLayers as it's the only one backed by OSGeo as far as I know). It also wouldn't hurt to get familiar with GeoServer or MapServer if you wish to provide OGC-standardized services.

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