Principal Data Engineer

BWXT • Oak Ridge, TN

18d • $86,450 - $136,000 • Onsite

About The Position

As Principal Data Engineer for Semantic Modeling & Industrial Knowledge Architecture, you will lead the design, engineering, and governance of large-scale semantic data models and industrial knowledge systems that support real-time operational intelligence in advanced manufacturing. You will build unified namespaces, enterprise knowledge graphs, and ontology-driven architectures to promote consistent interpretation, traceability, and interoperability of mission-critical industrial data. The role partners with controls engineers, data architects, AI/ML teams, and manufacturing subject-matter experts to build foundations for analytics, predictive maintenance, simulation workflows, and AI-driven automation in a high-security DOE/NNSA environment.

This position is on-site at BWXT’s Oak Ridge, Tennessee site and requires a Q clearance. Candidates must be U.S. citizens and be able to obtain and maintain the required DOE security clearance. The expected base salary range is $86,450–$136,000 per year; the offered salary may vary based on job-related and market factors. Total compensation may include an annual cash incentive and medical, retirement, and other benefits. New hires must meet applicable workplace health and safety requirements and successfully complete pre-employment checks and drug screening.

Requirements

• Bachelor’s degree in Engineering, Physics, Computer Engineering, Information Science, Computer Science, or a related STEM field.
• At least 5 years of experience in data engineering, semantic modeling, ontology design, or knowledge graph development.
• Experience designing ontologies using OWL, RDF, RDFS, SKOS, SHACL, and SPARQL.
• Hands-on experience with pipelines for structured, semi-structured, streaming, and unstructured data.
• Experience integrating OT/IoT, edge devices, and SCADA systems into unified data environments.
• Strong knowledge of data architecture principles and modern storage paradigms.
• Proficiency in Python, Java, or Scala.
• Familiarity with cloud platforms and Kubernetes.
• Understanding of semantic governance and lifecycle management.
• Must be a U.S. citizen and able to obtain and maintain a DOE Q clearance and DOE security clearance.
• Successfully complete a pre-employment check and drug screening.
• Adhere to applicable company health and safety requirements in the workplace.

Nice To Haves

• Master’s degree in Engineering, Computer Science, Robotics, Information Science, or a related field.
• Experience in automotive, aerospace, chemical, or high-throughput manufacturing sectors.
• Consulting or enterprise-scale modernization experience.
• Experience with Unified Namespace, data mesh/fabric, and graph-enabled AI frameworks.
• Familiarity with Databricks, Snowflake, or AWS analytics.
• Experience with semantic modularization, versioning, and interoperability.
• Experience leading global engineering teams.

Responsibilities

• Design semantic data models and ontologies for consistent interpretation of industrial data across chemical, aerospace, automotive, and high-throughput manufacturing sectors.
• Develop enterprise knowledge graph components using OWL, RDF, RDFS, SKOS, SHACL, and SPARQL.
• Build unified namespaces aligned with ISA-95 and W3C standards to support cross-system interoperability.
• Architect scalable storage solutions, including time-series databases, knowledge graphs, and lakehouse systems.
• Engineer pipelines to ingest, reconcile, and contextualize structured, semi-structured, streaming, and unstructured data.
• Apply governance practices including versioning, modularization, URI strategies, and quality gates.
• Map relational models into semantic representations using R2RML/RML.
• Support AI/ML and simulation teams by embedding semantic models into analytics and automation workflows.
• Lead workshops with subject-matter experts to define domain concepts, modeling requirements, and competency questions.
• Implement data quality controls, lineage, validation frameworks, and anomaly detection.
• Develop reusable frameworks for units modeling, hierarchical structures, identifier normalization, and event modeling.
• Mentor engineering teams and contribute to enterprise data modernization strategies.
• Maintain semantic specifications, governance artifacts, workflows, and compliance documentation.
• Participate in DOE/NNSA audits, reviews, and inspections.

Benefits

• Competitive salary and benefits.
• Flexible work schedules and paid time off, as applicable.
• Professional development, including mentorship and continuing education support.
• Annual cash incentive may be included in total compensation.
• Medical and retirement benefits, with plan details provided with an employment offer.
• Continuous learning and training for career growth.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal