Geospatial data platform for federal and emergency-response operations
SpatialGIS is a 20-person geospatial specialist firm (Maryland-based, founded 2016) serving federal, state, and local agencies. The stack—Python, R, Spark, Kubernetes, AWS infrastructure—reflects a data-heavy, containerized architecture built for scale. Current project focus spans labeling automation, DevOps pipeline work, and cloud migration, while pain points cluster around compliance (ATO authorization, accreditation), legacy system modernization, and compute cost control. The senior-skewed hiring mix (12 of 20) suggests operational maturity and domain depth over volume scaling.
SpatialGIS provides geospatial analytics and emergency-response data solutions to federal, state, local, and commercial clients. Core services include imagery analysis, remote sensing, data transformation, custom mapping, and enterprise GIS deployment. The firm operates as a mission-driven, small-team model (2–10 core staff according to LinkedIn size, though active hiring spans 20 roles), with engineering and data disciplines dominating the org. Work centers on cloud-hosted geospatial pipelines, data labeling infrastructure, and Kubernetes-based application deployment. Government compliance and security (ATO, vulnerability scanning) are operational baselines.
Python, R, Java, Apache Spark, Kubernetes, AWS (EMR, EKS, Lambda, VPC), Terraform, Docker, Trino, OpenSearch, and Jenkins for CI/CD. Tableau and custom web APIs for visualization and cloud integration.
DevOps pipeline automation, Kubernetes containerization of Java and Python services, data labeling and imagery analysis software, automated testing frameworks, and cloud API development. Also modernizing legacy systems and securing cloud adoption for federal compliance.
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