Graph Data Engineer
US$85,000 – US$105,000 / yr base salary
Posted 24 Aug 2026
Advertised as “Junior Graph Data Engineer”
BranchFactor Summary
The original posting with the fluff stripped out
- Management level
- Individual contributor
- Employment
- On-Site
- Contract
- Full-time
| Base salary | US$85,000 – US$105,000 / yr |
|---|
Study levelBachelor's
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 Junior Graph Data Engineer to help build, scale, and maintain the Enterprise Semantic Map — our ontology-grounded metadata graph.
In this role, you will help move the enterprise beyond traditional, static cataloging by supporting an automation-first approach. You will develop programmatic data and API integrations, help configure graph database structures, and support emerging agentic workflows that discover and catalog disparate data sources across the enterprise. Working alongside graph, data, and engineering teams, you will help 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 foundational coding skills and a systems-thinking mindset: an ability to understand how data pipelines and tool integrations affect the broader enterprise architecture, search and discovery, and downstream agentic research workflows and use cases.
Key Responsibilities
1. Automated Source Discovery & Metadata Ingestion (Technical Metadata)
- Supplying the “Raw Ingredients” for the Semantic Knowledge Graph: Assist in designing and deploying 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.