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Knowledge Graph Jobs in Virginia (NOW HIRING)

BTI360

NLP Engineer - Clearance Required

Herndon, VA · On-site

Responsibilities : • Lead end-to-end knowledge graph and knowledge base development efforts from problem definition to production, designing pipelines that extract, normalize, link, and organize ...

BTI360 Inc

NLP Engineer with Security Clearance

Herndon, VA · On-site

$117K - $141K/yr

As an NLP Engineer at BTI360, you will: • Lead end-to-end knowledge graph and knowledgebase development efforts from problem definition to production, designing pipelines that extract, normalize ...

Platinum Technologies

Senior Database Engineer

Herndon, VA · On-site

$109K - $148K/yr

... and knowledge graph platforms. The Sneior Database Engineer serves as the technical authority for the design, implementation, optimization, and administration of relational, graph, and semantic ...

Platinum Technologies

Senior Database Engineer

Herndon, VA · On-site

$109K - $148K/yr

... and knowledge graph platforms. The Sneior Database Engineer serves as the technical authority for the design, implementation, optimization, and administration of relational, graph, and semantic ...

Platinum Technologies

Senior Database Engineer

Herndon, VA · On-site

$109K - $148K/yr

... and knowledge graph platforms. The Sneior Database Engineer serves as the technical authority for the design, implementation, optimization, and administration of relational, graph, and semantic ...

Vantor

Database Engineer (TS/SCI)

Herndon, VA · On-site

... knowledge graph platforms. The Database Engineer serves as the technical authority for the design, implementation, optimization, and administration of relational, graph, and semantic database ...

Redhorse Corp.

Full Stack Developer

Arlington, VA · On-site

$160 - $190/hr

Support the integration of the knowledge graph database and enterprise analytics tools, AI reasoning chains, and mission data. * Develop scalable microservices and containerized applications.

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Knowledge Graph information

What is a knowledge graph?

A Knowledge Graph job typically involves designing, building, and maintaining structured representations of data that map relationships between entities. Professionals in this role work with technologies like RDF, SPARQL, ontologies, and graph databases to enhance data integration, retrieval, and reasoning. These jobs are common in AI, search, and data science fields, helping organizations improve knowledge discovery and decision-making.

What are some typical daily responsibilities of a knowledge graph engineer?

As a Knowledge Graph Engineer, your typical day involves designing and developing ontologies, integrating diverse data sources, and implementing graph-based data models to enhance information accessibility. You may work closely with data scientists, software developers, and business analysts to gather requirements and translate them into scalable knowledge graph solutions. Regular tasks include writing SPARQL queries, performing data mapping, maintaining documentation, and troubleshooting graph data issues. Collaboration and ongoing learning are integral as this field rapidly evolves with new tools and best practices.

What are the key skills and qualifications needed to thrive in the knowledge graph position, and why are they important?

To thrive as a Knowledge Graph Engineer, you need strong skills in semantic web technologies, ontology modeling, and data integration, typically supported by a background in computer science or data science. Familiarity with tools like RDF, SPARQL, OWL, and knowledge graph platforms (e.g., Neo4j, GraphDB) is common, and certifications in data engineering or semantic technologies are beneficial. Effective communication, problem-solving abilities, and cross-functional collaboration are valuable soft skills in this field. These competencies are crucial for designing, implementing, and maintaining knowledge graphs that enable advanced data discovery and insights for organizations.

What are the most commonly searched types of Knowledge Graph jobs in Virginia?

The most popular types of Knowledge Graph jobs in Virginia are:

What are popular job titles related to Knowledge Graph jobs in Virginia?

For Knowledge Graph jobs in Virginia, the most frequently searched job titles are:

What job categories do people searching Knowledge Graph jobs in Virginia look for?

The top searched job categories for Knowledge Graph jobs in Virginia are:

Knowledge Graph jobs near you
Infographic showing various Knowledge Graph job openings in Virginia as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 24% Part Time, and 2% Contract. Highlights an 89% Physical, 3% Hybrid, and 8% Remote job distribution.

Junior Graph Data Engineer

Redhorse Corporation

Arlington, VA • On-site

Other

Posted 13 days ago

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 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.

The salary range provided for this position represents the anticipated base salary for successful candidates. Actual compensation will be determined based on a variety of factors, including relevant experience, education, certifications, skills, security clearance level, geographic location, market conditions, and internal equity. In addition to base salary, eligible employees may participate in Redhorse's comprehensive benefits programs and may be eligible for performance-based or other incentive compensation, where applicable.
Redhorse Corporation is an equal opportunity employer. All qualified applicants will receive consideration for employment and will not be discriminated against on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, disability, or any other protected class.
If you are a qualified individual with a disability or a disabled veteran, you may request a reasonable accommodation if you are unable or limited in your ability to access job openings or apply for a job on this site as a result of your disability. You can request reasonable accommodations by contacting Talent Acquisition at
Redhorse Corporation shall, in its discretion, modify or adjust the position to meet Redhorse's changing needs. This job description is not a contract and may be adjusted as deemed appropriate in Redhorse's sole discretion.

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