A guide to geospatial tech roles and how to prepare for them

The geospatial industry is booming, offering diverse opportunities for professionals with technical expertise in data, software, and AI/ML. Whether you're interested in backend development, AI/ML, or data science, understanding different roles and how to prepare for them is essential. Navigating the landscape of geospatial tech roles can be tricky. This article breaks down key roles in the geospatial sector, their responsibilities, required skills, and how to land a job in each. We will delve deeper into the following roles: Backend Software Engineer, AI/ML Engineer, Data Scientist, Data Engineer, Data Analyst, Solution Architect, and DevOps Engineer.

While all the above roles contribute to the geospatial ecosystem, their focuses differ significantly. Think of it as building a house: some focus on the foundation, others on the plumbing, and some on the interior design.

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1. Defining the Roles

1.1. Backend Software Engineer

• Focus: Building and maintaining the backend infrastructure of geospatial applications. This involves developing APIs, databases, and services that process and deliver geospatial data. This also includes deploying and maintaining production-ready systems.

• Key Skills: Proficiency in languages like Python and tools such as docker; experience with cloud platforms (AWS, Azure, GCP); strong understanding of data structures, algorithms, and databases (e.g., PostgreSQL, MongoDB, and spatial databases).

• Preparation: Focus on building robust backend systems, learning about geospatial data formats (GeoJSON, Shapefile, netcdf, zarr, etc.), and exploring API development frameworks (e.g., Flask, REST, etc.).