Data Ontology Architect

We are seeking a Semantic Data Architect to lead the design and operationalization of our enterprise data governance framework. You will own data cataloging, end-to-end data lineage, and governance policy implementation, ensuring our data assets are trustworthy, discoverable, and compliant across all domains. Design and implement enterprise ontologies, semantic models, taxonomies, and knowledge graphs to support data governance and AI-driven business applications. Define and manage enterprise data lineage, metadata management, data cataloging, and semantic interoperability standards across platforms. Develop governance frameworks for data quality, stewardship, classification, ownership, compliance, and lifecycle management. Architect conceptual, logical, and physical data models aligned with enterprise architecture, governance standards, and business requirements. Design and support scalable data products enabling trusted, reusable, and domain-driven data consumption across analytics and AI platforms. Develop and implement Medallion Architecture data models (Bronze, Silver, Gold layers) for scalable and governed enterprise data platforms. Design and develop Databricks Data Vault solutions including hubs, links, satellites, historization, and lineage tracking for enterprise analytics and governance use cases. Architect semantic data models and ontology frameworks to improve data discoverability, traceability, and contextual understanding. Build and integrate knowledge graphs, metadata repositories, vector databases, and enterprise data platforms for contextual AI and analytics. Collaborate with business, governance, engineering, and AI teams to establish enterprise-wide data standards and domain models. Implement ontology alignment, schema mapping, and master/reference data management across complex enterprise systems. Design and support AI-driven data governance workflows including lineage tracking, policy enforcement, access control, and auditability. Develop agentic AI solutions using frameworks such as LangGraph, AutoGen, and CrewAI to automate metadata enrichment, governance, and workflow orchestration. Ensure observability and monitoring of data and AI systems through lineage tracing, metadata tracking, and operational dashboards. Apply governance and security controls including prompt injection defense, role-based access control, and secure data handling practices. Optimize semantic and governance platforms for scalability, reliability, compliance, and production deployment. Build CI/CD processes for ontology releases, governance workflows, metadata pipelines, and AI deployments. Stay current with emerging trends in data governance, metadata management, semantic web technologies, knowledge graphs, and agentic AI best practices. Minimum 5 years of experience in data management roles with a focus on data governance, ontology, data cataloging, and data lineage. Hands-on experience deploying and operating at least one enterprise data catalog platform (Collibra, Alation, DataHub, OpenMetadata, Purview, or equivalent). Deep expertise in data lineage extraction and representation: column-level, table and system lineage, impact analysis, root-cause tracing across ETL/ELT pipelines. Strong knowledge of data governance frameworks (DAMA-DMBOK, DCAM) and how to operate them in large organizations. Experience with metadata management: technical metadata, operational metadata, business glossaries, and ontology design. Proficiency in Python, SQL, PySpark and familiarity with cloud data platforms (Snowflake, BigQuery, Databricks, Redshift). Experience integrating governance tooling with data pipelines (dbt, Spark, Airflow, Informatica, or equivalent). Strong stakeholder management skills — ability to drive governance adoption with both technical and non-technical audiences. Minimum 2 years of AI engineering experience focused on LLM/agent systems in production. Experience with at least one agent framework (LangChain/LangGraph, AutoGen, CrewAI, Semantic Kernel, or equivalent). Experience with graph databases (Neo4j, Neptune) for lineage storage and traversal. Experience working with XML-based ETL and integration tools such as IBM InfoSphere DataStage, Informatica PowerCenter, and Alteryx for enterprise data integration, transformation, and workflow automation. Strong understanding of OpenLineage standards and lineage frameworks for capturing, tracking, and governing end-to-end data pipeline metadata and lineage across enterprise platforms