Graph Data Engineer

The role

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.

All jobs

Graph Data Engineer

Redhorse Corporation, Arlington, VA

Posted pay

$140K–$170K

a year

Compared with similar jobs

This base pay is near the median for data engineering jobs in other US places with no level in the job title.

Median: $155K a year
Other jobs in the group: 188

We compare the middle of each posted range. This is base pay as posted. The group does not include this job.

Location: Arlington, VA
Work type: Onsite
Level: Not given
Skills in the job text: Python
SQL
Java
Posted: Aug 24, 2026
Last check: 3 h ago
Job type: Data engineering

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Redhorse Corporation

Open tech jobs: 47
With posted pay: 100%
Median posted base: $150K–$200K

About Redhorse Corporation: funding, hiring and pay

Redhorse Corporation

About Redhorse Corporation: funding, hiring and pay

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The role

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.

What you will do

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.
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.
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.
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.
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.

Graph Data Engineer

The role

Graph Data Engineer

The role

What you will do

What you need

Nice to have

About Redhorse Corporation

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