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• Supplying the “Raw Ingredients” for the Semantic Knowledge Graph: Design, build, and deploy automated pipelines that programmatically discover enterprise data assets and interface with existing data catalogs. Scan, catalog, and ingest technical metadata — including schemas, tables, columns, and API endpoints — from legacy, cloud, and distributed environments to establish baseline assets for alignment to the Enterprise Core Ontology.
• Scaling the Semantic Map: Establish the automated pipelines and orchestrated workflows that ingest metadata at scale, replacing manual, field-by-field mapping. Own the practices that keep the ontology current as a dynamic, living “semantic control plane” rather than a static document.
• Establishing the Entry Point for Lineage: Define how the technical origin of ingested data is registered and how metadata is captured at the point of ingestion, creating the foundation for automated provenance chains that track where data originated and how it changes over time.
• Ontological Alignment: Lead the alignment of discovered data elements from local systems to the shared Enterprise Core Ontology and specialized Domain Ontologies, with particular attention to compatibility with established institutional frameworks (e.g., DIA’s DIKEM). Preserve local naming conventions while establishing standardized, shared meaning, and resolve modeling conflicts as they arise.
• Lineage Tracking: Design and maintain data lineage chains within the Provenance Layer, applying industry lineage standards to document where data originates, how it is transformed, and who governs it.
• Graph Querying & Validation: Write, optimize, and review graph queries supporting metadata retrieval, logical validation, and graph manipulation. Establish reusable query patterns and validation checks the wider team can build on.
• Big-Picture Integration: Assess how newly integrated data sources and automated pipelines affect the broader Enterprise Semantic Map, selected use cases, downstream consumers, and enterprise search and discovery — and adjust the design accordingly.
• Downstream Enablement: Connect data assets to relevant mission metadata so technical capabilities can be clearly linked to the mission workflows they support.
• Governance Compliance: Ensure enterprise assets are associated with appropriate governance metadata, including ownership, classifications, handling rules, and access constraints. Translate complex data policies into machine-readable semantic structures.
• Semantic Control Plane Ownership: Maintain and optimize the Enterprise Semantic Map within enterprise graph database platforms so human analysts, applications, and autonomous AI agents can efficiently search, navigate, and discover resources. Tune schema and query performance as the graph grows.
• Agent Integration: Partner with AI engineers so planning, research, and tool agents can dynamically query the graph, and help define the grounded, trustworthy reasoning and retrieval strategies those agents depend on.
• Mentorship: Guide junior engineers on graph modeling, query construction, and pipeline development, and review their work.
• Design Documentation & Advocacy: Document schema decisions, modeling rationale, and runbooks so the design is reproducible, and represent technical positions clearly to architects, program leadership, and government stakeholders.

• Ontology & Semantic Standards: Working experience with formal ontology or semantic web standards (e.g., RDF, OWL, SHACL) and with established government- or defense-related semantic models.
• Agentic AI & AI Frameworks: Experience with LLM orchestration, retrieval-augmented generation, or agentic workflows, particularly where a graph provides grounding.
• Data Lineage & Metadata Standards: Applied experience with open lineage specifications or metadata management frameworks.
• Data Catalogs & Stewardship: Experience with metadata catalog environments and data stewardship systems.
• Workflow Orchestration: Experience with pipeline scheduling and orchestration tooling.
• Cloud & Deployment: Familiarity with cloud data platforms, containerized deployment, and CI/CD practices.
• Mission Domain Exposure: Prior experience supporting defense, intelligence community, or other regulated enterprise data environments.