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# Principal Software Engineer / Data Engineer (Onsite)

- **Company:** RTX
- **Location:** Richardson, TX, United States
- **Experience:** Expert
- **Salary:** $107,500.0 - $204,500.0
- **Contract:** Permanent contract
- **Skills:** Agile Methodology, Artificial Intelligence, Amazon Web Services, Microsoft Azure, Data Governance, Query Languages, DevOps, Programming Tools, Graph Database, Linked Data, Machine Learning, Meta-Data Management, Neo4j, NoSQL, Operational Data Store, Resource Description Framework (RDF), Data Mesh, Semantic Web, SPARQL, SQL Databases, Enterprise Data Management, Digital Twin, Feature Engineering, Delivery Pipeline, Large Language Models, Generative AI, Knowledge Representation, Microsoft Fabric, Data Lineage, Cosmos DB, Machine Learning Operations, Tools for Reporting, Physical Data Models
- **Published:** October 22, 2026
- **Apply:** https://www.dice.com/job-detail/07fb91fc-e344-4919-ad34-11bde9f4a63b

## About the Role

* Typically requires a degree in Science, Technology, Engineering or Mathematics (STEM) and 8 years of relevant experience.
 * Experience in using OWL, RDF, RDFS, SPARQL, and related W3C semantic web standards.
 * Experience with ontology development tools such as Protg, TopBraid Composer, or similar.
 * Experience in developing conceptual, logical, and physical data models, as well as canonical data designs
 * Experience with graph databases (e.g., Stardog, Neo4j, Amazon Neptune, Azure Cosmos DB for Gremlin) and graph query languages.
 * Experience collaborating in cross-functional Agile or DevOps environments.
 * Active and transferable TS/SCI U.S. government issued security clearance is required prior to start date.

Qualifications We Prefer

 * Experience with data fabric, data mesh, Lakehouse architectures, and cloud platforms (Azure, AWS).
 * Background supporting digital thread, digital twin, and AI/ML programs.
 * Experience mentoring or leading data modeling teams.
 * Knowledge of linked data principles and experience publishing or consuming linked datasets.
 * Exposure to ML/AI pipelines and practical experience enhancing them through semantic enrichment.
 * Experience with LLM-grounded knowledge graphs or retrieval-augmented generation (RAG) pipelines.
 * Excellent analytical thinking with ability to translate ambiguous business requirements into formal semantic models.
 * Demonstrated ability to collaborate with cross-functional teams to model complex business domains.
 * Strong skills in SQL, SPARQL, Cypher, and relational/NoSQL databases.
 * Strong understanding of data governance, metadata management, and master data management (MDM) principles.

## Description

RTX is seeking a Data Engineer for an immediate position onsite in our Richardson, TX or Dulles, VA office. The Data Engineer will be a key contributor to our program, responsible for designing semantic data models, ontologies, and knowledge graphs that serve as the connective tissue between raw operational data and AI-ready intelligence., * Design, build, and maintain enterprise ontologies and knowledge graphs using standards such as OWL, RDF, and SPARQL.

 * Collaborate with domain experts, data engineers, and business stakeholders to capture and formalize business concepts, relationships, and rules.
 * Develop and manage taxonomies, controlled vocabularies, and semantic metadata schemas across operational and business domains.
 * Integrate ontological structures with enterprise data platforms, AI/ML pipelines, and analytics environments.
 * Partner with AI/ML Engineers and data scientists to ensure ontology structures support downstream model training, feature engineering, and inference pipelines.
 * Define and enforce data governance standards for entity definitions, lineage tracking, and metadata management across enterprise platforms.
 * Monitor ontology performance and usage patterns; proactively identify gaps and drive continuous improvement.
 * Support integration of ontology layers with enterprise data products, dashboards, and reporting tools.
 * Evaluate and implement ontology management tools and platforms, ensuring scalability and interoperability.
 * Translate complex technical concepts into clear documentation for both technical and non-technical audiences.
 * Stay current with advancements in knowledge representation, semantic web technologies, and AI/ML techniques.