What are some of the skills and tools that you need to master as a spatial data engineer or manager?

1 Spatial data engineering

Spatial data engineering is the process of designing, developing, and maintaining spatial data infrastructure (SDI). SDI is composed of hardware, software, data, standards, and policies that enable the creation, storage, access, and analysis of spatial data. To be a successful spatial data engineer, one must have a strong background in computer science, programming, databases, and spatial data formats and standards. Additionally, spatial data engineers should be familiar with various sources of spatial data such as remote sensing, GPS, web mapping, and crowdsourcing. Common tasks for spatial data engineers include developing and testing spatial data models and schemas; implementing and optimizing spatial databases and data warehouses; building and deploying spatial web services and APIs; integrating and harmonizing spatial data from different sources and platforms; developing and applying spatial data quality and security measures; automating and streamlining spatial data workflows and pipelines.

2 Spatial data management

Spatial data management is the process of organizing, maintaining, and updating spatial data throughout its lifecycle. Spatial data managers need to have a strong understanding of spatial data requirements, users, and applications, as well as strong skills in data governance, documentation, metadata, and archiving. Common tasks for spatial data managers include defining and enforcing spatial data policies and standards, cataloging and indexing spatial data and metadata, managing access and distribution of the data, monitoring usage and performance of the data, updating and migrating the data and systems, and educating and supporting users.

3 Spatial data tools and techniques

There are numerous tools and techniques available for spatial data engineering and management. Depending on your needs and preferences, you can select from a variety of software, frameworks, libraries, and languages that support spatial data processing and analysis. PostgreSQL/PostGIS is an open-source relational database management system with a powerful spatial extension that supports various spatial data types, functions, and indexes. QGIS is an open-source desktop GIS application that allows you to view, edit, analyze, and visualize spatial data from various sources and formats. GeoServer is an open-source server that provides web services for spatial data such as WMS, WFS, WCS, and WMTS. GDAL/OGR is an open-source library that provides a common interface for reading and writing various spatial data formats such as Shapefile, GeoJSON, GeoTIFF, and KML. Python is a general-purpose programming language with many modules and packages for spatial data analysis like NumPy, Pandas, GeoPandas, Shapely, Fiona, PyProj, Rasterio, and PyQGIS. R is a statistical programming language with many packages for spatial data analysis like sf, sp, raster, rgdal, rgeos, and leaflet.

5 Spatial data career paths

If you aspire to have a career in spatial data engineering or management, you will need technical, analytical, and interpersonal skills. Additionally, you must have a passion for spatial data and its applications. You can choose from various career paths depending on your education, experience, and goals. These include spatial data engineer, analyst, scientist, manager, consultant, and educator. A spatial data engineer designs and maintains infrastructure and systems. A spatial data analyst performs analysis and visualization for various purposes. A spatial data scientist applies statistical and machine learning methods to spatial data problems. A spatial data manager oversees the organization of resources and services. A spatial data consultant provides expert advice on projects and issues. Lastly, a spatial data educator teaches concepts and skills related to spatial data.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?