About this role
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