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We are looking for a Senior Ontologist to lead the design, development, and operationalization of buildings ontologies and taxonomies that power data interoperability, analytics, and intelligent systems across connected buildings products.

This role is hands-on and strategic. You will work at the intersection of domain modeling, semantic technologies, and standards, shaping how complex data is represented, connected, and consumed at scale.

You will collaborate closely with domain experts, data engineers, platform architects, and product teams to ensure that semantic models are accurate, extensible, and aligned with industry standards and real-world operational needs.

Honeywell Technologies is a global, pure-play automation company with a legacy of innovating to help solve the world's most mission-critical challenges, enhancing the quality of life for people and communities around the world. We serve the building, industrial and process sectors with a broad portfolio of services, solutions and products, underpinned by our Honeywell Technologies Accelerator operating system and Honeywell Technologies Forge intelligence layer. By combining the deep domain expertise of our more than 50,000 employees with decades of data from our global installed base, we are uniquely positioned to lead the industrial sector's transition from automation to autonomy.

Required Qualifications

Core Expertise

• Deep, hands-on experience in ontology engineering and taxonomy design for industrial or building domains.
• Strong working knowledge of Brick Schema, Project Haystack, and IFC (not just theoretical familiarity).
• Proven experience building real-world, production-grade semantic models.
• Understanding of Large Language model along with structured knowledge of graphs for semantic backbone creation

Technical Skills

• Expert-level proficiency in OWL 2, RDF, RDFS, SPARQL, SHACL, SKOS, JSON-LD, and Turtle.Semantic Web Stack:
• Deep expertise in at least two of: Neo4j, Amazon Neptune, Stardog, GraphDB, Virtuoso, Ontotext, TigerGraph.Graph Databases:
• Familiarity with semantic querying (e.g., SPARQL, CIPHER) and metadata-driven architectures.
• Familiarity with cloud data stacks (AWS, GCP, Azure), Apache Kafka, dbt, Databricks, or Snowflake.Data Platforms:
• Experience with OWL reasoners (Pellet, HermiT, FaCT++) and rule-based systems (SWRL, RIF).Reasoning Engines:
• Familiarity with knowledge graph platforms like Palantir Foundry, Microsoft Fabric, or Google Enterprise Knowledge Graph.
• Ability to collaborate effectively with software and data engineers.
• Understanding of how industrial systems generate, structure, and consume data.
• Experience with digital twins, asset modeling and systems engineering.
• Experience designing ontology governance frameworks on a scale.
• Ability to evaluate and integrate open vs proprietary semantic models.
• Prior experience in a platform, product, or enterprise-scale environment.
• Experience working in a fast-paced technology environment focused on delivering a world class product within an agile methodology utilizing latest technology frameworks

Key Responsibilities

Ontology & Semantic Model Development

• Design, build, and maintain industrial ontologies, taxonomies, and knowledge models covering assets, spaces, processes, and operational data.
• Develop and extend models aligned with industry standards such as:
  • Brick Schema
  • Project Haystack
  • ASHRAE 233P
  • IFC (Industry Foundation Classes)
  • Related building, utilities, energy, or asset-management ontologies
• Define clear concept hierarchies, relationships, constraints, and naming conventions.
• Conduct ontology alignment and integration with external knowledge bases and domain-specific ontologies.

Standards & Interoperability

• Map, align, and reconcile concepts across multiple industry schemas and customer-specific models.
• Design semantic alignment strategies between heterogeneous data sources (BMS, IoT, SCADA, CMMS, ERP, digital twins).
• Ensure models support interoperability, extensibility, and backward compatibility.
• Leverage large language models (e.g., GPT-4, Claude, LLaMA, Mistral) and NLP pipelines to automate ontology population, entity extraction, and relation classification

Applied Semantics & Engineering Collaboration

• Work closely with data engineering and platform teams to:
  • Operationalize ontologies in production systems
  • Support semantic querying, reasoning, and metadata-driven pipelines
• Define best practices for ontology versioning, governance, and lifecycle management.
• Translate abstract semantic models into practical, implementable artifacts.

Architecture Leadership & Strategy

• Define the long-term technical vision and roadmap for the enterprise semantic and knowledge graph platform.
• Establish architectural standards, design patterns, and reference architectures for semantic data integration across business units.
• Partner with data engineering, ML, product, and business teams to translate domain requirements into graph and semantic models.
• Evaluate and recommend emerging technologies, tools, and open standards in the knowledge graph and AI/LLM landscape.
• Represent the organization in external technical communities, standards bodies, and industry working groups.