Overview

We work with terabytes of terrain, hydrography, and imagery, and much of the value comes from organizing it so a modeler can actually find and use it. At Lynker, we build the hydrologic and environmental data behind water models used nationwide, and we deliver it the way modern geospatial teams do: as cloud-native data and live products, not static files and one-off analyses. These two roles build that pipeline and the products that run on top of it.

 

We are hiring two Geospatial Scientists with strong software development skills. One role leans toward data infrastructure: STAC catalogs, cloud-optimized formats (Cloud Optimized GeoTIFFs, GeoParquet, and Zarr), and scalable processing pipelines. The other leans toward operational products: turning that data into hydrologic and environmental layers, services, and regularly updated scientific products. Most of the work is in Python, and a background in hydrology or environmental science is a strong advantage. Candidates with deep strengths in one area and an interest in growing into the other are encouraged to apply; please indicate where your strengths lie.

 

Responsibilities

Duties of the Geospatial Scientist will include the following:

 

SHARED RESPONSIBILITIES:

 

• Software development: write and maintain clean, tested Python that runs reliably over large areas.
• Large-scale data: work with large raster and vector datasets using cloud-native formats and storage.
• Collaboration: build things other teams can use, working with the Platform, Science, Modeling, and Applications teams.

 

E   EMPHASIS ONE:  DATA PIPELINES & CLOUD-NATIVE INFRASTRUCTURE

E

 

• Catalogs & formats: build and maintain STAC catalogs and cloud-optimized data (COG, GeoParquet, Zarr) so large datasets are searchable and quick to access.
• Pipelines: write reproducible pipelines that process terrain, hydrography, and imagery at regional to continental scale, using distributed compute.
• Services: stand up and run the services that publish and tile the data, such as pgSTAC and TiTiler, and keep them reliable and versioned.

 

     EMPHASIS TWO:  GEOSPATIAL PRODUCTS & ENVIRONMENTAL ANALYSIS

 

• Products: turn data into hydrologic and environmental map layers, and help move models into operational scientific products and forecasts that are regularly updated.

• Analysis: apply remote sensing and, where appropriate, machine learning to environmental and land-cover mapping.

• Usable outputs: make outputs that are clear and usable for scientists, partners, and downstream applications.

Qualifications

The Geospatial Scientist selected should have the following:

• Master's in geospatial science, geography, environmental or earth science, hydrology, computer science, or a related field; or a Bachelor's plus 2 years building geospatial data pipelines, tools, or cloud-native services; or equivalent hands-on experience in place of a degree.
• Strong Python and solid software habits: Git, testing, and code review.
• Real experience with cloud-native geospatial formats and services, and hands-on AWS experience (our primary cloud); GCP or Azure experience also welcome.
• Solid geospatial fundamentals: projections, raster and vector data, and common GIS tools.

 

 

 

The Ideal Geospatial Scientist will have the following:

 

• Experience with the STAC ecosystem and tools such as pgSTAC, stac-fastapi, or TiTiler.
• Distributed processing with Dask, Spark, cloud batch, or Google Earth Engine.
• The modern geospatial Python stack: xarray, rasterio, geopandas, and formats like COG, GeoParquet, and Zarr.
• Infrastructure-as-Code and CI/CD for AWS deployments (e.g., Terraform or CloudFormation, Docker, GitHub Actions).
• Turning models or analyses into operational, regularly updated products or forecasts.
• Machine learning and remote sensing for environmental or land-cover work.
• Background in hydrology or environmental science.
• Colorado Front Range presence for periodic in-person collaboration.