Senior Technical Project Manager/Ontologist

RTX•New York, NY

22h•$132,400 - $251,600•Remote

About The Position

The RTX Corporate, Enterprise Services Data organization is seeking a technical business analyst focused on ontology and semantic data modeling. The role establishes a foundation for enterprise analytics, automation, and AI by designing scalable domain ontologies and semantic layers that provide shared, trusted meaning across corporate functions. Reporting to the Associate Director of Data and AI, the position uses enterprise systems and business knowledge to connect data across functions so employees and AI systems can discover, understand, and consistently interpret enterprise data. The role collaborates with ontologists, data engineers, data stewards, AI teams, and functional experts across Finance, Supply Chain, Risk, Business Solutions, and HR. The position is remote and primarily performed offsite from home. Depending on proximity to an RTX site, the manager may discuss some onsite presence. Candidates may be asked to attend selected interview steps in person. The role requires a U.S. Person and has no security clearance requirement. The stated salary range is $132,400–$251,600 USD.

Requirements

A university degree or equivalent experience and at least 10 years of relevant experience, or an advanced degree in a related field and at least 7 years of experience.
At least 5 years of hands-on experience in ontology engineering, knowledge graphs, or semantic data modeling.
Experience with relational and dimensional data modeling, including SQL skills for profiling and mapping source data.
Experience translating complex or ambiguous business terminology into precise, reusable data definitions.

Nice To Haves

Master of Science degree in Analytics.
Strong communication skills, including communicating with technical and non-technical audiences and facilitating workshops with subject matter experts.
Strong knowledge of semantic web standards, including RDF, RDFS, OWL, SKOS, SHACL, and SPARQL.
Experience in aerospace, defense, or complex manufacturing, particularly with product structures, bills of materials, configuration management, or supply chain data.
Hands-on experience with graph databases and platforms such as Kobai, GraphDB, or Amazon Neptune.
Experience with semantic layers and metrics stores, including dbt Semantic Layer, AtScale, Databricks Unity Catalog metrics, or Snowflake semantic views.
Experience using knowledge graphs to ground large language models through GraphRAG, entity linking, or ontology-guided extraction.
Python programming experience automating ontology pipelines with tools such as rdflib or owlready2.
Experience creating and delivering enterprise data products.
Demonstrated ability to lead change, influence stakeholders without direct authority, and build alignment across cross-functional teams.
Experience using data-driven insights to identify opportunities, measure adoption, and develop improvement plans.

Responsibilities

Design scalable functional ontologies for business concepts such as parts, programs, suppliers, contracts, assets, employees, and cost centers, including their relationships, properties, and business rules.
Develop and maintain semantic layers by mapping ontology concepts to enterprise source systems, including SAP, Databricks, Snowflake, ERP, PLM, MES, HR, and Finance platforms.
Develop knowledge graphs, taxonomies, and metadata to support LLMs, retrieval-augmented generation, and agent-based applications.
Establish ontology modeling standards for naming conventions, version control, change management, and quality checks, and review models developed by other teams.
Partner with business functions and subject matter experts to facilitate workshops, capture business definitions and rules, and resolve conflicting definitions.
Support data governance initiatives with data stewards, including business glossaries, data lineage, classification, and access policies, including applicable ITAR/EAR requirements.
Measure ontology coverage and adoption and evaluate the effect of semantic layers on data reuse, consistency, and AI accuracy.
Drive change and adoption by influencing stakeholders, building cross-functional alignment, and explaining the value of enterprise ontology and semantic data solutions.
Manage multiple initiatives and priorities and deliver against established timelines and objectives through disciplined execution and effective communication.
Coach and support team members and business champions through knowledge sharing and enablement.