GIS Data Intern

Tactacam•Billings, MT

About The Position

Tactacam is seeking a GIS Data Intern to support the Habitat IQ business unit and the geospatial work that powers the Habitat IQ mobile and web app. Reporting to the Habitat IQ Business Unit Leader and working day-to-day with the responsible Product Owner and software engineering teams, you will help source and curate geospatial data, support mapping features across the app, and contribute to the data foundation that enables automatic identification of features on our interactive maps — such as houses, roads, crops, land cover, water, and parcels. You will work across multiple workstreams: sourcing and evaluating data, preparing datasets for use in the app and AI models, supporting spatial analysis, and helping engineers integrate geospatial data into production systems. This is a hands-on role ideal for a student or early-career candidate with a passion for maps, data, and applied geospatial work. The ideal candidate is pursuing (or recently completed) a degree in GIS, geography, data science, environmental science, or a related field, and has strong curiosity, attention to detail, and the initiative to dig into unfamiliar datasets and tools.

Requirements

Currently pursuing or recently completed a Bachelor's or Master's degree in GIS, geography, geospatial science, data science, environmental science, remote sensing, or a related field.
Coursework or hands-on experience with GIS tools such as QGIS, ArcGIS, or equivalent.
Familiarity with common geospatial data formats (Shapefile, GeoJSON, GeoTIFF, KML, GeoPackage) and coordinate reference systems.
Basic scripting ability in Python (geopandas, rasterio, shapely) or willingness to learn quickly.
Strong attention to detail and ability to evaluate data quality critically.
Curiosity and resourcefulness in tracking down and cultivating data sources.
Clear written communication for documenting sources, methods, and findings.
Ability to work collaboratively across product, engineering, and data teams.

Nice To Haves

Experience working with aerial or satellite imagery and remote sensing datasets, preferred.
Familiarity with open geospatial data sources such as OpenStreetMap, Microsoft Building Footprints, Overture Maps, USDA Cropland Data Layer, or NAIP imagery, preferred.
Exposure to training or evaluating AI/computer vision models on geospatial imagery (object detection, segmentation, classification), preferred.
Familiarity with parcel, cadastral, or agricultural datasets, preferred.
Experience with SQL and spatial databases (PostGIS), preferred.
Exposure to web mapping libraries (Mapbox, Leaflet, MapLibre, ArcGIS JS API) or tile services, preferred.

Responsibilities

Research, evaluate, and cultivate geospatial data sources — public (USGS, USDA, NOAA, Census, state and county open data), open (OpenStreetMap, Microsoft Building Footprints, Overture Maps), and commercial (aerial/satellite imagery, parcel, ag, and POI providers) — for use in the Habitat IQ mobile and web app.
Support feature work in the Habitat IQ app that relies on geospatial data, including auto-identification of map features (houses, roads, crops, land cover, water, parcels), boundary and overlay layers, points of interest, routing, and location search.
Download, clean, transform, and organize geospatial datasets into usable formats (GeoJSON, Shapefile, GeoTIFF, GeoPackage, Parquet) using QGIS, ArcGIS, or Python-based tools.
Perform quality checks on geospatial data — spot-checking features against imagery, comparing datasets, and flagging gaps, errors, or coverage issues.
Assist in preparing labeled datasets for training and evaluating AI/computer vision models that work with maps and imagery.
Support software engineering teams with data ingestion, transformation, and integration into production systems, APIs, and map tiles/layers used by the app.
Perform ad-hoc spatial analysis and mapping requests from the Business Unit Leader, Product Owner, engineering teams, and other stakeholders.
Build and maintain an inventory of data sources, including metadata, licensing terms, refresh cadence, and known limitations.
Document data sources, processing steps, and findings so the work is repeatable and transferable to other team members.
Stay up to date on new and emerging geospatial datasets, tools, and techniques, and share findings with the team.