Graph Data Engineer

NextGenEnergyJobs; Arlington, Northern (VA, KY); Hybrid; USD 140,000 - 200,000; Full time

Job summary

Redhorse Corporation seeks a senior data/semantic engineer to design, build, and optimize automated pipelines that discover enterprise data assets and interface with data catalogs.

You will own the semantic map, establish provenance, align ontologies with the Enterprise Core Ontology, govern lineage, and collaborate with AI engineers to enable trusted agent queries.

Qualifications

• Experience with formal ontology or semantic web standards (RDF, OWL, SHACL).
• Proven ability to implement metadata management at scale using catalogs and lineage.
• Experience in defense/regulatory enterprise data environments.

Responsibilities

• Design, build, and deploy automated pipelines to discover enterprise data assets.
• Ingest, catalog, and normalize technical metadata from legacy, cloud, and distributed sources.
• Define how data lineage/provenance is captured at ingestion and tracked over time.
• Align discovered data elements to the Enterprise Core Ontology and Domain Ontologies.
• Maintain and document the Provenance Layer for data lineage across systems.
• Write, optimize, and review graph queries for metadata retrieval and validation.
• Collaborate with AI engineers to enable grounded reasoning using the graph.
• Mentor junior engineers on graph modeling and pipeline development.
• Document schema decisions and runbooks for architects and leadership.

Skills

Ontology & Semantic Standards

LLM Orchestration

Data Lineage

Metadata Management

Graph Databases

Tools

RDF/OWL/SHACL

Graph Databases (Neo4j)

Job description

Now is a great time to join Redhorse Corporation.

Key Responsibilities

• 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.

Requirements

• 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.