Data Ontologist

Fractional Data Architect / Ontologist

Enterprise Data Strategy & Semantic Architecture

Role Summary

A large financial services firm is seeking a senior Data Architect / Ontologist to engage in a fractional, time-bound capacity to help define and accelerate its enterprise data strategy. The organization is investing in data governance and centralized data architecture, and needs specialized expertise to ensure that work is built on a modern semantic foundation rather than retrofitted after the fact.

This individual will operate as both a strategic advisor and a hands-on practitioner , responsible for establishing the ontological and semantic frameworks that connect governance, architecture, and AI readiness. The engagement is structured around knowledge transfer and enablement : build the foundation, establish best practices, coach internal teams, and hand it back.

Core Responsibilities

Semantic Architecture & Ontology Design

• Define and implement enterprise ontologies, taxonomies, and controlled vocabularies aligned to core business domains
• Design the semantic context layer that bridges structured data, metadata, and business meaning
• Establish patterns for knowledge graph development and multi-dimensional semantic modeling
• Align semantic architecture to the existing data platform (Snowflake) and future AI/agent use cases

Data Governance Integration

• Connect governance artifacts (definitions, lineage, ownership, classification) to semantic models so governance becomes executable, not just documented
• Introduce metadata standards that enrich data with the context required for downstream automation and AI
• Ensure governance and architecture efforts are built in parallel, not sequentially

Enterprise Data Architecture Guidance

• Advise on the transition from siloed, system-specific architecture to a centralized, enterprise-aligned model
• Identify architectural gaps across data modeling, semantic integration, and knowledge representation
• Provide reference architectures and design patterns the internal team can adopt and extend

Internal Enablement & Knowledge Transfer

• Work directly alongside existing data architects, engineers, and governance practitioners
• Coach, mentor, and pair with internal staff to transfer ontology and semantic modeling expertise
• Produce reusable standards, playbooks, and reference materials that persist after the engagement ends
• Operate with a clear exit trajectory : defined handoff milestones and sustainability criteria

Executive Communication Support

• Help leadership articulate the business case for semantic architecture and ontology investment
• Translate technical concepts into clear, executive-ready narratives that connect data foundations to AI outcomes
• Support internal positioning of the initiative to decision-makers who may not have deep data backgrounds

Required Qualifications

• 10+ years of progressive experience across taxonomy, ontology, metadata management, and/or data architecture
• Demonstrated expertise in:
• Ontology design and semantic modeling (OWL, RDF, SKOS, or equivalent frameworks)
• Taxonomy and controlled vocabulary development at enterprise scale
• Metadata standards definition and implementation across multiple systems and business units
• Experience in financial services or similarly complex, multi-entity environments
• Proven ability to lead master data and data governance initiatives , including stakeholder identification, data landscape mapping, and cross-functional alignment
• Familiarity with modern data platforms (e.g., Snowflake) and emerging knowledge graph, AI, and agent-based architectures
• Strong documentation and standards-publishing skills: ability to produce best practices guides, reference architectures, and enablement materials that are clear, reusable, and self-sustaining

Preferred Qualifications

• Background in information science, library science, or a related discipline that provides formal grounding in classification, taxonomy, and knowledge organization
• Experience operating at the VP / Director level in data strategy, ontology, or enterprise platforms
• Track record of building and handing off capability to internal teams rather than creating long-term consulting dependency
• Ability to operate credibly at both the executive and practitioner level within the same engagement