Senior Data Engineer - Graphs

About Peraton

Peraton is a next-generation national security company that drives missions of consequence spanning the globe and extending to the farthest reaches of the galaxy. As the world's leading mission capability integrator and transformative enterprise IT provider, we deliver trusted, highly differentiated solutions and technologies to protect our nation and allies. Peraton operates at the critical nexus between traditional and nontraditional threats across all domains: land, sea, space, air, and cyberspace.

The company serves as a valued partner to essential government agencies and supports every branch of the U.S. armed forces. Each day, our employees solve the most daunting challenges that our customers face. Visit  to learn how we're keeping people around the world safe and secure.

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.

• 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

• 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…