> Markdown version of [/jobs/ext/537683-senior-data-engineer-knowledge-graphs](https://www.wearedevelopers.com/jobs/ext/537683-senior-data-engineer-knowledge-graphs). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Engineer - Knowledge Graphs - **Company:** Peraton Inc - **Location:** Annapolis Junction, MD, United States - **Experience:** Expert - **Salary:** $135,000.0 - $216,000.0 - **Contract:** Permanent contract - **Skills:** JavaScript (Programming Language), Artificial Intelligence, Computational Linguistics, Data Architecture, Information Engineering, Data Transformation, Query Languages, Decision Support Systems, Document-Oriented Databases, Graph Database, Python (Programming Language), Metadata, Meta-Data Management, Neo4j, Parsing, Search Technologies, Software Engineering, SQL Databases, TypeScript, Unstructured Data, Data Processing, Knowledge Representation, Information Technology, Data Pipelines - **Published:** June 13, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=0f7bd693bd96b3d9 ## About the Role 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., * 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, * 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 ## Description 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., * 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 ## Related Videos - [A Data Mesh needs Open Metadata](https://www.wearedevelopers.com/videos/505-a-data-mesh-needs-open-metadata) - [Tips and Tricks for Working with JSON](https://www.wearedevelopers.com/videos/1229-tips-and-tricks-for-working-with-json) - [Putting the Graph In GraphQL With The Neo4j GraphQL Library](https://www.wearedevelopers.com/videos/257-putting-the-graph-in-graphql-with-the-neo4j-graphql-library) - [Scaling GraphRAG: Efficient Knowledge Retrieval for AI](https://www.wearedevelopers.com/videos/100025-scaling-graphrag-efficient-knowledge-retrieval-for-ai) - [Cyber Sleuth: Finding Hidden Connections in Cyber Data](https://www.wearedevelopers.com/videos/893-cyber-sleuth-finding-hidden-connections-in-cyber-data) - [Graphs and RAGs Everywhere... 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