Senior Data Engineer - Knowledge Graphs

Peraton•Herndon, VA

20h

About The Position

Peraton Labs is seeking a Senior Data Engineer to help design, build, and operationalize the data foundations supporting advanced AI-enabled capabilities. This role will focus on transforming complex structured and unstructured information into graph-aware, semantically meaningful data products that can support analytics, reasoning, retrieval, and agentic workflows. We are looking for a candidate who combines strong data engineering execution with meaningful experience in knowledge graphs, semantic representations, NLP-derived structure, and graph-based analysis. This may come from a traditional data engineering background with hands-on knowledge graph experience, or from a research-oriented knowledge graph / semantic systems background paired with proven implementation ability. The ideal candidate for this role should be comfortable working across data pipelines, semantic modeling, graph representations, and AI-enabled data architectures. You should be comfortable moving between concept and implementation, helping shape how knowledge is extracted, structured, linked, and made usable for downstream AI systems.

Requirements

Minimum of BS with 12+ years of experience, MS with 10+ YoE, or PhD with 7+ YoE 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 within one or more of the following areas: 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/GraphDB platforms
Experience working with both structured and unstructured data in support of 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 where both rigor and adaptability matter
Strong written and verbal communication skills, with the ability to explain technical tradeoffs clearly across engineering and non-engineering stakeholders
US Citizenship is a requirement for this position

Nice To Haves

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

Responsibilities

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 areas such as 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