Junior Graph Data Engineer

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.
• 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.

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

• 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.

3. Enterprise Systems Thinking & Alignment

• Big-Picture Integration: Evaluate how newly integrated data sources and automated pipelines affect the broader Enterprise Semantic Map, selected use cases, downstream consumers, and enterprise search and discovery.
• Downstream Awareness: Connect data assets to relevant mission metadata so technical capabilities can be clearly linked to the mission workflows they support.
• Governance Compliance: Help ensure enterprise assets are associated with appropriate governance metadata, including ownership, classifications, handling rules, and access constraints. Support the translation of complex data policies into machine-readable semantic structures.

4. Smart Search & Agent Enablement

• Semantic Control Plane Maintenance: Help 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.
• Agent Integration Support: Collaborate with AI engineers to help planning, research, and tool agents dynamically query the graph and build grounded, trustworthy reasoning and retrieval strategies.

Required Experience/Clearance

• Bachelor’s Degree with 1+ 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)
• Knowledge graph concepts, including nodes, edges, relationships, and metadata schemas
• Foundational Data Engineering: Basic understanding of data structures, databases, and how data moves through pipelines or ETL (Extract, Transform, Load) processes.
• Systems-Thinking Mindset: Ability to understand how individual data pipelines connect to and support a broader enterprise ecosystem.
• Attention to Detail: Precision in aligning metadata terms, formatting data endpoints, and maintaining technical schemas.
• Collaboration & Communication: Ability to take direction from senior engineers, document work clearly, and explain technical decisions to non-specialist stakeholders.

Preferred Qualifications

• Graph Query Languages: Exposure to - or willingness to learn - graph query languages for metadata retrieval and validation (e.g., Cypher for property graphs, SPARQL for RDF/triple stores).
• Graph Database Platforms: Conceptual familiarity with modern enterprise graph database platforms.
• Agentic AI & AI Frameworks: Basic conceptual understanding of, coursework in, or project experience with LLM orchestration or agentic workflows.
• Data Lineage & Metadata Standards: Exposure to open lineage specifications or metadata management frameworks.
• Standard Ontologies & Semantic Models: Conceptual familiarity with established government- or defense-related semantic models that support standardized enterprise data integration.
• Data Catalogs: Familiarity with metadata catalog environments and data stewardship systems.
• Workflow Orchestration: Exposure to pipeline scheduling and orchestration tooling.