Junior Graph Data Engineer

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.

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.

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.

• 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.
• Scaling the Semantic Map:
  Use automated pipelines and orchestrated workflows to ingest metadata at scale rather than relying on manual, field-by-field mapping. Help keep the ontology current as a dynamic, living semantic control plane rather than a static document.
• Establishing the Entry Point for Lineage:
  Register the technical origin of ingested data and capture metadata at the point of ingestion. This creates the foundation for automated provenance chains that track where data originated and how it changes over time.

• Ontological Alignment Support:
  Collaborate with senior engineers to align 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.
• Lineage Tracking Support:
  Help engineering teams construct and maintain data lineage chains within the Provenance Layer, following applicable industry lineage standards to document where data originates, how it is transformed, and who governs it.
• Graph Querying Support (Growth Area):
  As your technical skills develop, write and test basic graph queries to support metadata retrieval, logical validation, and graph manipulation.

• Big-Picture Integration:
  Evaluate how newly integrated data…