Enterprise Data Architect (Ontology and Semantic Modeling)

Responsibilities

• Define and implement enterprise ontologies, taxonomies, and controlled vocabularies aligned to core business domains
• Design a semantic context layer that connects structured data, metadata, and business meaning
• Establish patterns and standards for knowledge graph development and semantic modeling
• Align semantic architecture with an existing data platform, including Snowflake, and support emerging artificial intelligence and agent-based use cases
• Integrate data governance artifacts such as definitions, lineage, ownership, and classification into semantic models to enable executable governance
• Introduce and standardize metadata frameworks that enrich data for automation and downstream analytics
• Ensure data governance and architecture initiatives evolve in parallel and remain tightly aligned
• Provide guidance on transitioning from siloed systems to a centralized, enterprise-wide data architecture
• Identify and address gaps in data modeling, semantic integration, and knowledge representation
• Develop reference architectures, reusable design patterns, and best practice documentation for internal adoption
• Collaborate closely with data architects, engineers, and governance teams to transfer knowledge and build internal capability
• Create playbooks, standards, and documentation that enable long-term sustainability after engagement completion
• Define clear handoff milestones and exit criteria to ensure continuity
• Support executive stakeholders by translating complex technical concepts into clear, business-focused narratives
• Help articulate the value of semantic architecture and its impact on data strategy and artificial intelligence readiness

Required experience and skills

• 10 or more years of experience in data architecture, ontology design, taxonomy development, or metadata management
• Strong expertise in semantic modeling frameworks such as OWL, RDF, SKOS, or equivalent approaches
• Experience designing and implementing enterprise-scale taxonomies and controlled vocabularies
• Proven ability to define and operationalize metadata standards across multiple systems and business units
• Background in financial services or similarly complex, multi-entity environments
• Experience leading data governance and master data initiatives, including stakeholder alignment and data landscape analysis
• Familiarity with modern data platforms such as Snowflake and knowledge graph architectures
• Understanding of artificial intelligence and agent-based systems in relation to data architecture
• Strong documentation and communication skills, with the ability to produce reusable standards, frameworks, and architectural guidance
• Ability to operate at both strategic and hands-on levels, guiding implementation while advising leadership
• Experience mentoring and enabling internal teams through coaching, pairing, and knowledge transfer

FAQ

1. What are the core responsibilities of an Enterprise Data Architect specializing in ontology and semantic modeling?
This role is responsible for designing enterprise-wide data architectures that enable consistent understanding, integration, and governance of business information. Responsibilities include developing ontologies, defining semantic models, establishing data standards, and creating frameworks that improve data interoperability across systems and domains.

2. What is ontology and how is it used in enterprise data architecture?
Ontology is a structured representation of business concepts, entities, relationships, and rules within a domain. In enterprise environments, ontologies help create a shared understanding of data across departments, applications, and analytics platforms. They support knowledge management, AI initiatives, and semantic search capabilities.

3. What is semantic modeling and why is it important?
Semantic modeling organizes data around business meaning rather than technical system structures. It enables consistent definitions, improves data discoverability, and enhances communication between technical and business stakeholders. Semantic models help organizations build scalable and reusable data ecosystems.

4. How does this role support enterprise data governance?
The architect develops frameworks that standardize business definitions, metadata, and relationships across the organization. By aligning semantic models with governance policies, the role improves data quality, consistency, lineage, and compliance. Governance becomes more effective when business meaning is clearly defined.

5. What technologies and standards are commonly used in this role?
Common technologies include knowledge graphs, metadata management platforms, graph databases, and semantic web technologies. Standards such as RDF, OWL, SKOS, and SPARQL may be used to represent and query semantic relationships. Cloud-based data platforms and governance tools are often integrated into the architecture.