Data Ontology Architect

Job description

Job Description: 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. - Responsibilities: 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. - Qualifications: 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