Senior Data Engineer - Graphs

Required Qualifications

• 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 with in 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/Type Script, and SQL for data transformation and pipeline development, plus familiarity with graph and semantic tooling such as Neo4j/Neptune/Graph
  
  DB 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 Graph
  
  RAG or related graph-enhanced retrieval architectures
• Familiarity with graph databases, triple stores, 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

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.

Key responsibilities may include, but are not limited to:

• 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…