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Redhorse

Government consulting with AI-driven digital transformation

Junior Graph Data Engineer

Data Management +1Confirmed live in the last 24 hours
Full-timeArlington County, Arlington, VA, USA$85k - $105k /yrIn-person

About the Job

Requirements

  • A bachelor's degree with at least 1 year of relevant professional experience, or equivalent, is required.
  • An active TS/SCI clearance is required.
  • Foundational proficiency in programming and scripting for automation, such as Python, Java, or a comparable general-purpose language, is required.
  • Relational database querying, such as SQL, is required.
  • Knowledge of structured and semi-structured data formats, such as JSON, XML, and YAML, is required.
  • Knowledge graph concepts, including nodes, edges, relationships, and metadata schemas, is required.
  • A basic understanding of data structures, databases, and how data moves through pipelines or extract, transform, and load processes is required.
  • The ability to understand how individual data pipelines connect to and support a broader enterprise ecosystem is required.
  • Precision in aligning metadata terms, formatting data endpoints, and maintaining technical schemas is required.
  • The ability to take direction from senior engineers, document work clearly, and explain technical decisions to non-specialist stakeholders is required.

Responsibilities

  • 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.
  • Use automated pipelines and orchestrated workflows to ingest metadata at scale and help keep the ontology current as a dynamic semantic control plane.
  • Register the technical origin of ingested data and capture metadata at the point of ingestion to establish provenance chains that track data origins and changes over time.
  • Collaborate with senior engineers to align discovered data elements from local systems to the shared Enterprise Core Ontology and specialized Domain Ontologies, including compatibility with frameworks such as DIA’s DIKEM, preserving local naming conventions while establishing standardized shared meaning.
  • Help engineering teams construct and maintain data lineage chains within the Provenance Layer, following applicable industry lineage standards to document data origins, transformations, and governance.
  • As technical skills develop, write and test basic graph queries to support metadata retrieval, logical validation, and graph manipulation.
  • 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.
  • Connect data assets to relevant mission metadata so technical capabilities can be linked to the mission workflows they support.
  • Help ensure enterprise assets are associated with appropriate governance metadata, including ownership, classifications, handling rules, and access constraints, and support translating data policies into machine-readable semantic structures.
  • Help maintain and optimize the Enterprise Semantic Map within enterprise graph database platforms so analysts, applications, and autonomous AI agents can search, navigate, and discover resources.
  • Collaborate with AI engineers to help planning, research, and tool agents dynamically query the graph and build grounded, trustworthy reasoning and retrieval strategies.

Desired Qualifications

  • Exposure to, or willingness to learn, graph query languages for metadata retrieval and validation, such as Cypher for property graphs and SPARQL for RDF/triple stores.
  • Conceptual familiarity with modern enterprise graph database platforms.
  • Basic conceptual understanding of, coursework in, or project experience with large language model orchestration or agentic workflows.
  • Exposure to open lineage specifications or metadata management frameworks.
  • Conceptual familiarity with established government- or defense-related semantic models that support standardized enterprise data integration.
  • Familiarity with metadata catalog environments and data stewardship systems.
  • Exposure to pipeline scheduling and orchestration tooling.

About the Company

Redhorse

Government consulting with AI-driven digital transformation

Redhorse helps government agencies improve operations by providing consulting and hands-on delivery that combines digital transformation, artificial intelligence, and sector expertise in National Security, Networking Technology and Infrastructure, Energy, and the Environment. The company works with clients on mission-critical projects, analyzing data and systems and then designing architectures, implementing technology changes, and guiding long-term partnerships in regulated environments. Its differentiators are close, collaborative client work, sector-specific knowledge, and a focus on outcomes that align with public sector security and compliance needs. The goal is to help government agencies operate more efficiently, securely, and effectively through data-driven strategies and AI-enabled transformations.

Company size
201-500
Company stage
Growth Equity (Venture Capital)
Total funding
Not listed
Headquarters
Arlington County, VA
Founded
2008

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