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Senior Data Engineer - Knowledge GraphsPeraton • Herndon, Virginia, United States
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Senior Data Engineer - Knowledge Graphs

Peraton
  • US
    Herndon, Virginia, United States
  • US
    Herndon, Virginia, United States
Jetzt Bewerben

Über

Senior Data Engineer - Knowledge Graphs Job Locations: US-MD-Annapolis Junction | US-VA-Herndon | US-VA-Reston
Responsibilities Peraton Labs is seeking a Senior Data Engineer to design, build, and operationalize the data foundations supporting advanced AI‑enabled capabilities. This role focuses on transforming complex structured and unstructured information into graph‑aware, semantically meaningful data products that support analytics, reasoning, retrieval, and agentic workflows.
We seek a candidate with strong data engineering execution and experience in knowledge graphs, semantic representations, NLP‑derived structure, and graph‑based analysis.
The ideal candidate should be able to work across data pipelines, semantic modeling, graph representations, and AI‑enabled data architectures, moving between concept and implementation, shaping how knowledge is extracted, structured, linked, and made usable for downstream AI systems.
Design, build, and maintain scalable data pipelines supporting graph‑based and AI‑enabled workflows.
Develop data models and processing approaches that transform raw structured and unstructured data into semantically meaningful graph‑oriented representations.
Contribute to the creation, enrichment, and operationalization of knowledge graphs supporting retrieval, reasoning, entity relationships, and advanced analytics.
Support ingestion, normalization, linking, and transformation of data into graph‑compatible formats such as RDF and related semantic representations.
Apply experience in NLP, AMR, UMR, semantic parsing, graph analysis, or ontology‑informed data modeling to improve how information is structured and connected.
Build data pipelines and engineering workflows that support graph‑centric applications, including AI‑enabled search, contextual retrieval, and decision support.
Partner with AI/ML, platform, and software engineering teams to ensure graph and semantic data assets are usable within production‑oriented systems.
Help define approaches for entity resolution, relationship extraction, semantic enrichment, metadata management, and graph quality validation.
Contribute to architectures that support agentic AI workflows by enabling richer data context, structured memory, and relationship‑aware information access.
Work with a mix of structured, semi‑structured, and unstructured data sources to improve interoperability and downstream usability.
Support graph analysis and exploration efforts that inform system design, data relationships, and capability development.
Ensure data engineering solutions are maintainable, scalable, and aligned to operational and mission needs.
Document data flows, graph models, transformation logic, and engineering decisions clearly for technical stakeholders.
Qualifications Required Qualifications
Minimum of BS with 12+ years of experience, MS with 10+ years of experience, or PhD with 7+ years of experience in data engineering, knowledge graph engineering, semantic systems, NLP‑enabled data processing, or related technical roles.
Strong hands‑on experience building and maintaining data pipelines in modern engineering environments.
Demonstrated experience with knowledge graphs, graph data models, or semantic data architectures.
Experience in RDF, graph analysis, semantic representation, ontology‑informed data modeling, AMR, UMR, or NLP‑driven structured extraction.
Strong hands‑on experience with Python, JavaScript/TypeScript, and SQL for data transformation and pipeline development, plus familiarity with graph and semantic tooling such as Neo4j, Neptune, or GraphDB platforms.
Experience working with both structured and unstructured data to support downstream analytics or AI/ML use cases.
Ability to translate complex source data into usable, high‑quality representations for graph‑based or semantic systems.
Strong understanding of data quality, schema design, metadata, transformation logic, and scalable data workflows.
Ability to operate effectively in highly technical environments where requirements may evolve and both rigor and adaptability matter.
Strong written and verbal communication skills, with the ability to explain technical trade‑offs clearly across engineering and non‑engineering stakeholders.
US Citizenship is a requirement for this position.
Preferred Qualifications
Experience with agentic AI systems or workflows that rely on structured context, memory, planning, or relationship‑aware retrieval.
Experience with GraphRAG or related graph‑enhanced retrieval architectures.
Familiarity with graph databases, triplestores, semantic query languages, or related tooling.
Experience supporting entity resolution, relationship extraction, semantic search, or contextual retrieval workflows.
Background in NLP, semantic parsing, knowledge representation, or computational linguistics.
Experience designing systems that connect knowledge representation approaches to operational AI applications.
Familiarity with ontology development, schema alignment, or semantic interoperability challenges.
Exposure to mission, government, defense, or regulated technical environments.
Advanced degree in computer science, data science, computational linguistics, AI/ML, or a related field.
Target Salary Range $135,000 - $216,000. This represents the typical salary range for this position. Salary is determined by various factors, including but not limited to, the scope and responsibilities of the position, the individual's experience, education, knowledge, skills, and competencies, as well as geographic location and business and contract considerations. Depending on the position, employees may be eligible for overtime, shift differential, and a discretionary bonus in addition to base pay.
EEO EEO: Equal opportunity employer, including disability and protected veterans, or other characteristics protected by law.
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  • Herndon, Virginia, United States

Sprachkenntnisse

  • English
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