Geospatial Engineer

steampunk•McLean, VA

12h

About The Position

Overview We are seeking a Geospatial Engineer to design, build, and modernize geospatial data pipelines and services that support mission-critical analytics, mapping, and spatial decision-support applications. This role focuses on engineering scalable, cloud-native geospatial workflows and re-architecting legacy GIS pipelines , such as those built in ArcGIS Desktop or ArcGIS Enterprise into modern cloud platforms. The Geospatial Engineer will work closely with data engineers, cloud engineers, application teams, and mission stakeholders to ensure geospatial data is reliable, performant, and accessible for analytics, AI/ML, and operational use cases. Contributions Design and implement end-to-end geospatial data pipelines for ingesting, transforming, and serving vector, raster, and spatiotemporal datasets in cloud environments. Modernize and re-architect legacy GIS workflows built in ArcGIS Desktop, ArcGIS Pro, or ArcGIS Enterprise into cloud-native or hybrid architectures. Build geospatial ETL/ELT workflows using tools such as ArcGIS Data Interoperability, FME, GDAL, GeoPandas , or custom Python pipelines. Leverage cloud-native services (AWS, Azure, or GCP) to store, process, and serve geospatial data at scale (e.g., object storage, managed databases, serverless compute ). Implement spatial data models and indexing strategies using databases such as PostGIS , SQL Server Spatial, BigQuery GIS, or Azure SQL Spatial. Develop automated workflows for geospatial data validation, reprojection, tiling, generalization, and performance optimization. Support geospatial APIs, map services, and data products used by web applications, dashboards, and analytic platforms. Collaborate with Data Engineers and MLOps /AI teams to integrate geospatial features into analytics pipelines, feature stores, and ML models. Apply DevSecOps best practices to geospatial pipelines, including CI/CD, infrastructure-as-code, monitoring, logging, and secure access controls. Troubleshoot performance bottlenecks and data quality issues in large or complex spatial datasets. Document geospatial architectures, pipelines, schemas, and operational runbooks to support maintainability and reuse. Stay current with modern GIS, cloud geospatial services, and open-source spatial tooling to continuously improve delivery practices. You will contribute to the growth of our AI & Data Exploitation Practice!

Requirements

Ability to hold a position of public trust with the U.S. government.
Bachelor’s degree in Geographic Information Systems (GIS), Geospatial Engineering, Geography, Computer Science, Data Engineering, or a related field.
2–5 years of experience working with geospatial data, GIS systems, or spatial data pipelines.
Hands-on experience with Esri ArcGIS products (ArcGIS Pro, ArcGIS Enterprise, ArcGIS Online) and familiarity with their data models and services.
Experience building or modernizing geospatial pipelines using Python, GDAL, GeoPandas , Rasterio , or similar libraries.
Working knowledge of spatial databases such as PostGIS , SQL Server Spatial, Oracle Spatial, or cloud-native spatial services.
Experience with at least one cloud platform (AWS, Azure, or GCP) and related data services (object storage, serverless compute , managed databases).
Familiarity with geospatial web services and standards (WMS, WMTS, WFS, REST APIs, GeoJSON , STAC).
Understanding of coordinate systems, projections, spatial indexing, and performance considerations for large spatial datasets.
Experience with containerization, CI/CD pipelines, or infrastructure-as-code is a plus.
Strong problem-solving skills and ability to collaborate across engineering, analytics, and mission teams.

Nice To Haves

Esri Technical Certifications (ArcGIS Pro / Enterprise)
AWS Cloud Practitioner or Data Analytics Specialty
Azure Data Engineer Associate
GCP Professional Data Engineer

Responsibilities

Design and implement end-to-end geospatial data pipelines for ingesting, transforming, and serving vector, raster, and spatiotemporal datasets in cloud environments.
Modernize and re-architect legacy GIS workflows built in ArcGIS Desktop, ArcGIS Pro, or ArcGIS Enterprise into cloud-native or hybrid architectures.
Build geospatial ETL/ELT workflows using tools such as ArcGIS Data Interoperability, FME, GDAL, GeoPandas , or custom Python pipelines.
Leverage cloud-native services (AWS, Azure, or GCP) to store, process, and serve geospatial data at scale (e.g., object storage, managed databases, serverless compute ).
Implement spatial data models and indexing strategies using databases such as PostGIS , SQL Server Spatial, BigQuery GIS, or Azure SQL Spatial.
Develop automated workflows for geospatial data validation, reprojection, tiling, generalization, and performance optimization.
Support geospatial APIs, map services, and data products used by web applications, dashboards, and analytic platforms.
Collaborate with Data Engineers and MLOps /AI teams to integrate geospatial features into analytics pipelines, feature stores, and ML models.
Apply DevSecOps best practices to geospatial pipelines, including CI/CD, infrastructure-as-code, monitoring, logging, and secure access controls.
Troubleshoot performance bottlenecks and data quality issues in large or complex spatial datasets.
Document geospatial architectures, pipelines, schemas, and operational runbooks to support maintainability and reuse.
Stay current with modern GIS, cloud geospatial services, and open-source spatial tooling to continuously improve delivery practices.