Graph Engineer

The Center for Geospatial Solutions (CGS) is a self-sustaining nonprofit enterprise. Founded to help bridge the gap between policy and practice, our mission is to enable people and the planet to meet the pace of change by expanding access to new technologies that power more sustainable and equitable outcomes.

We are a fully remote team of award-winning professionals with decades of applied expertise and end-to-end GIS capabilities. By embracing whole-system thinking and state-of-the-art technology, we enable partners across public, private, and nonprofit sectors to tackle complex, real‑world challenges—like housing affordability, ecosystem conservation, water management, and sustainable infrastructure—with greater clarity.

Our work liberates and connects key information, creates nuanced pictures of complex situations, and makes data easier to use, understand, and act on. We use tools like satellite data and artificial intelligence to deliver insights for impact.

At CGS, we believe that technology can be a tool for positive change. We are dedicated to building a diverse team that represents the communities and systems we live and work in. If you’re excited about this role but don’t meet every listed qualification, we encourage you to apply. We value potential, curiosity, and lived experiences, and know a more inclusive team makes us a stronger organization.

CGS was established in 2020 at the Lincoln Institute of Land Policy which seeks to improve quality of life through the effective use, taxation, and stewardship of land. A nonprofit private operating foundation whose origins date to 1946, the Lincoln Institute researches and recommends creative approaches to land as a solution to economic, social, and environmental challenges. Through education, training, publications, and events, the Lincoln Institute integrates theory and practice to inform public policy decisions worldwide and has office locations in Cambridge, Massachusetts;
Washington, DC;
Phoenix, Arizona; and Beijing, China.

The Center for Geospatial Solutions is seeking a Knowledge Graph Engineer to serve as a lead technical contributor to the national Geoconnex water data indexing system, working in close collaboration with the Associate Director for Technology and software development team.

This role will contribute directly to the Internet of Water (IoW), a bold, coalition‑led effort to improve water data sharing to fundamentally transform water management. Since 2022, CGS has served as the official home of the IoW Coalition, advancing this shared vision by developing and promoting new tools and best practices that strengthen the nation's water data sharing systems, in partnership with public agencies, nonprofit organizations, and the private sector.

The ideal candidate will have deep expertise in semantic web technologies, water domain ontologies, and geospatial data systems, with a passion for advancing open data infrastructure that serves federal, state, and local stakeholders.

Reporting to the Associate Director for Technology for the Internet of Water, the Knowledge Graph Engineer will be responsible for advancing the technical capabilities of the Geoconnex knowledge graph, integrating cutting‑edge AI interfaces, and optimizing spatial query performance across heterogeneous water datasets. They will bring expertise in ontology development, semantic web standards, and water informatics while playing a critical role in scaling the Geoconnex system to serve as the foundational infrastructure for national water data.

• Design and implement ontological frameworks for water and geospatial data integration, building on existing standards and community best practices
• Architect and deploy large language model interfaces to the Geoconnex knowledge graph, enabling natural language querying and data discovery
• Optimize spatial query performance and develop advanced geospatial indexing strategies for water‑related features and datasets

• Collaborate with federal partners (USGS, US , USACE, EPA) and state agencies to integrate diverse water datasets into…