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# Graph Data Engineer

- **Company:** Redhorse Corporation
- **Location:** Arlington, VA, United States
- **Salary:** $140,000.0 - $170,000.0
- **Contract:** Permanent contract
- **Skills:** Query Performance, Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Cloud Computing, Computer Programming, Continuous Integration, Information Engineering, Extract Transform Load (ETL), Query Languages, Distributed Systems, Graph Database, JSON, Python (Programming Language), Metadata, Meta-Data Management, Metadata Repositories, Metadata Standards, Network Control, Operational Databases, Software Architecture, Cloud Services, Runbook, Semantic Web, SPARQL, SQL Databases, Systems Integration, Workflow Management Systems, Extensible Markup Language (XML), YAML, Enterprise Search, Enterprise Software Applications, Retrieval-Augmented Generation, Delivery Pipeline, Large Language Models, Semi-structured Data, Data Lineage, Data Management, Virtual Agents, Data Pipelines, Api Management, Web Api
- **Published:** August 24, 2026
- **Apply:** https://www.clearancejobs.com/jobs/9113977/graph-data-engineer

## About the Role

* Bachelor's Degree with 5+ of relevant professional experience or equivalent.
 * Active TS SCI Clearance.
 * Core Technical Skills: Foundational proficiency across the following areas, demonstrated in any comparable technology:
 + Programming and scripting for automation (e.g., Python, Java, or a comparable general-purpose language)
 + Relational database querying (e.g., SQL)
 + Structured and semi-structured data formats (e.g., JSON, XML, YAML)
 + Graph query languages for retrieval, validation, and manipulation (e.g., Cypher for property graphs, SPARQL for RDF/triple stores)
 + Knowledge graph concepts, including nodes, edges, relationships, and metadata schemas
 * Data Engineering Experience: Hands-on experience building and operating production data pipelines or ETL (Extract, Transform, Load) processes, including error handling, monitoring, and scheduling.
 * Graph Database Platforms: Practical experience with at least one enterprise graph database platform, including schema design and query performance considerations.
 * API & Systems Integration: Experience integrating heterogeneous systems through APIs across legacy, cloud, and distributed environments.
 * Systems-Thinking Mindset: Ability to reason about how individual pipelines and modeling choices propagate through a broader enterprise ecosystem, and to weigh trade-offs explicitly.
 * Attention to Detail: Precision in aligning metadata terms, formatting data endpoints, and maintaining technical schemas.
 * Communication & Stakeholder Engagement: Ability to explain semantic and architectural decisions to both engineering peers and non-technical mission stakeholders, and to document them durably., * 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., If you are a qualified individual with a disability or a disabled veteran, you may request a reasonable accommodation if you are unable or limited in your ability to access job openings or apply for a job on this site as a result of your disability. You can request reasonable accommodations by contacting Talent Acquisition at Talent_Acquisition@redhorsecorp.com

## Description

We are seeking an analytical, forward-thinking Graph Data Engineer to design, build, scale, and maintain the Enterprise Semantic Map - our ontology-grounded metadata graph.

In this role, you will move the enterprise beyond traditional, static cataloging by leading an automation-first approach. You will architect and deliver programmatic data and API integrations, design and configure graph database structures, and build the agentic workflows that discover and catalog disparate data sources across the enterprise. Partnering with graph, data, and engineering teams, you will align these assets to enterprise semantic and provenance layers so data is discoverable, understandable, trusted, and dynamically composable for human analysts, applications, and downstream AI agents.

Success in this role requires strong hands-on engineering skills and a systems-thinking mindset: the ability to reason about how data pipelines and tool integrations affect the broader enterprise architecture, search and discovery, and downstream agentic research workflows - and to make and defend design decisions that others will build on.

Key Responsibilities

 1. Automated Source Discovery & Metadata Ingestion (Technical Metadata)

*

 + 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.

 2. Semantic & Provenance Mapping (Semantic & Lineage Metadata)

*

 + 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.

 3. Enterprise Systems Thinking & Alignment

*

 + 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.

 4. Smart Search & Agent Enablement

*

 + 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.

 5. Technical Leadership & Mentorship

*

 + 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.


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