Graph Data Engineer

Redhorse Corp.; Arlington (VA); Hybrid; USD 110,000 - 165,000; Full time

Job summary

Redhorse Corporation is seeking a Graph Data Engineer to design, build, and maintain the Enterprise Semantic Map—an ontology-grounded metadata graph. You will create automated pipelines that discover enterprise data assets and interface with data catalogs, while architecting API integrations and graph structures to align assets with enterprise semantic layers.

The role requires hands-on engineering and systems thinking to optimize data pipelines, discovery, and downstream agentic workflows

Qualifications

• Experience designing and implementing graph-based data models.
• Building automated pipelines to ingest and catalog metadata.
• Strong programming and scripting skills (Python, SQL).

Responsibilities

• Automate source discovery and metadata ingestion; design pipelines to scan, catalog, and ingest technical metadata from diverse environments.
• Scale and maintain the semantic knowledge map; ensure ontology remains current and reusable.

Skills

Graph databases

Data modeling

ETL pipelines

Python

SQL

Cloud platforms

Education

Bachelor's degree in Computer Science or related field

Job description

About the Organization

Now is a great time to join Redhorse Corporation. We are a solution-driven company delivering data insights and technology solutions to customers with missions critical to U.S. national interests. We’re looking for thoughtful, skilled professionals who thrive as trusted partners building technology-agnostic solutions and want to apply their talents supporting customers with difficult and important mission sets.

Now is an exciting time to join Redhorse Corporation.

We are redefining how the U.S. Government transforms data into operational advantage through artificial intelligence, graph analytics, and mission-driven software engineering. Our teams work alongside the Department of Defense to build secure, scalable capabilities that enable analysts and decision-makers to move faster, reason better, and operate with greater confidence.

Our approach combines human-centered design, modern software engineering, graph technologies, artificial intelligence, and agile delivery to solve some of the nation’s most challenging problems.

About 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.

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.