Geospatial intelligence software and data engineering for defense and federal missions
RGi builds GEOINT software and analytics for defense, intelligence and federal programs out of Fairfax, Virginia. Its stack pairs a big-data tier (Spark, HDFS, Delta Lake, NiFi, Elasticsearch) with self-hosted infrastructure (OpenStack, Ceph, RHEL, Ansible), a mix typical of on-prem and classified deployments. Hiring is accelerating, skews senior and is almost entirely engineering, with job posts centered on classified cloud delivery and security compliance.
Reinventing Geospatial (RGi) is a privately held company founded in 2009 and headquartered in Fairfax, Virginia, with 51–200 employees. It delivers geospatial technology, data science and AI/ML work for defense, intelligence and federal government clients. Its work spans data collection, analytics, computer vision, visualization and UI/UX for analysts and operators who need a clearer picture of operational environments. The company also runs research and development efforts alongside larger mission programs, and hires within the United States.
RGi uses Python, Java, Clojure, Ruby, ArcGIS, Apache Spark, HDFS, Delta Lake, Apache NiFi, Elasticsearch, Kubernetes, Docker, GitLab CI/CD, Jenkins, AWS, OpenStack, Ceph and Red Hat Enterprise Linux.
Yes. RGi's hiring is accelerating and concentrated in engineering, mostly at senior level, with US-based roles focused on data pipelines, ML deployment, classified cloud microservices and DevSecOps.
Job posts point to geospatial activity-based intelligence analysis, large-scale data pipeline engineering, ML inference services at scale, enterprise GEOINT applications and microservices for classified cloud environments.
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Reinventing Geospatial (RGi)'s technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.