Enterprise Data Architect & Semantic Modeler

Univedge Consulting is hiring for one of our direct clients - a global technology consulting leader based in the US. Immediate interviews for the below position. Share your updated resume to apply.

We are seeking a hands-on Enterprise Data Architect and Semantic Modeler to help design, govern, and operationalize the enterprise data models, semantic structures, data contracts, and AI-ready data products that power the Enterprise Data Lakehouse on OCI and Oracle AIDP. In this role, you will work across canonical modeling, dimensional modeling, semantic layer design, business glossary alignment, metadata, lineage, data quality, governance, and consumption architecture to deliver trusted Bronze, Silver, and Gold data assets that support analytics, reporting, operational intelligence, GenAI, RAG, semantic search, and agentic business workflows.

This role reports to the Principal Data Engineering Lead and operates within the Data Governance, AI & Analytics organization as part of the Enterprise Data Lakehouse Architecture and Modeling team. The ideal candidate is a senior data architect with deep experience designing enterprise-grade data models, defining semantic standards, translating business meaning into governed data structures, and partnering with engineering, governance, platform, security, and business teams to deliver reliable, reusable, and AI-ready data products in a modern cloud lakehouse environment.

What You Will Do

·        Enterprise Data Architecture and Modeling: Design conceptual, logical, physical, canonical, dimensional, and semantic data models for priority enterprise domains across the OCI AIDP Lakehouse.

·        Define reusable modeling patterns for Bronze, Silver, and Gold layers, including raw ingestion alignment, curated domain models, conformed dimensions, certified metrics, and governed consumption structures.

·        Establish enterprise standards for keys, relationships, hierarchies, naming conventions, reference data, master data alignment, metadata, lineage, and model documentation.

·        Design canonical models and reusable domain entities that reduce duplicated definitions, improve interoperability, and enable consistent enterprise consumption.

·        Partner with platform architects, data engineers, Data Product Owners, governance leads, security teams, and business stakeholders to translate business requirements into scalable, governed, and implementable data architecture solutions.

Semantic Layer, Metrics, and Business Definitions

·        Design and maintain semantic models that connect business terms, entities, measures, dimensions, hierarchies, calculations, and certified metrics to trusted underlying data assets.

·        Partner with business stakeholders, domain owners, data stewards, and governance leads to define consistent business definitions, resolve conflicting terminology, and align data products to enterprise glossary standards.

·        Develop semantic structures that enable self-service analytics, governed BI, natural-language querying, GenAI, RAG, embeddings, vector search, and agentic workflow use cases.

·        Define standards for certified metrics, reusable calculations, consumption-ready views, semantic metadata, and business-friendly data product descriptions.

·        Ensure semantic models align with lineage, data quality evidence, access controls, PHI protection requirements, and approved enterprise governance standards.

Data Contracts and Governance Implementation

·        Define Data Contracts that specify schema expectations, business meaning, ownership, data quality rules, lineage requirements, freshness expectations, classification, and consumption constraints.

·        Translate governance policies into practical architecture and modeling controls that engineering teams can implement across lakehouse pipelines and data products.

·        Define metadata, catalog registration, source-to-target mapping, glossary, lineage, documentation, and certification requirements for governed data product publication.

·        Apply approved security, privacy, masking, PHI protection, and access control requirements to data models, semantic layers, and consumption patterns.

·        Ensure data products align with certified business definitions, semantic standards, data quality expectations, stewardship responsibilities, and enterprise publication requirements.

Data Product Architecture

·        Contribute to the design, certification, publication, and evolution of governed enterprise data products for analytics, reporting, operational, and AI-native consumption.

·        Define data product blueprints that include domain scope, canonical entities, semantic definitions, certified metrics, quality expectations, lineage, access requirements, and consumption patterns.

·        Support data product marketplace readiness by ensuring required metadata, glossary alignment, lineage, quality, SLA, security, and documentation artifacts are complete.

·        Partner with Data Product Owners and domain teams to understand consumption needs and design reusable, trustworthy, and AI-ready data assets.

·        Continuously improve architecture standards, modeling patterns, reusable templates, and governance-aligned delivery practices across the lakehouse lifecycle.

AI-Ready Semantic Architecture

·        Design AI-ready semantic and metadata patterns that support GenAI, RAG, embeddings, vector search, knowledge retrieval, semantic models, and agentic business workflows.

·        Define how business meaning, certified metrics, lineage, quality evidence, and governance constraints are exposed to AI-assisted and AI-native consumption patterns.

·        Use approved AI-assisted tools to accelerate modeling, documentation, glossary mapping, metadata enrichment, impact analysis, and architecture artifact creation.

·        Apply AI-native practices responsibly, ensuring AI-generated architecture, models, and documentation are reviewed, governed, accurate, secure, and production-ready.

What You Will Deliver

·        Enterprise conceptual, logical, physical, canonical, dimensional, and semantic data models for priority OCI AIDP Lakehouse domains.

·        Reusable modeling standards, naming conventions, entity definitions, relationship patterns, conformed dimensions, certified metrics, and semantic layer design guidance.

·        Data Contract templates and domain-specific contract definitions covering schema, ownership, quality rules, metadata, lineage, classification, and consumption expectations.

·        Governed data product blueprints for Bronze, Silver, and Gold assets, including scope, entity design, semantic definitions, quality expectations, access requirements, and publication criteria.

·        Business glossary alignment, certified metric definitions, semantic metadata, source-to-target mappings, lineage documentation, and catalog registration artifacts.

·        AI-ready data architecture assets that support analytics, business intelligence, GenAI, RAG, vector search, embeddings, semantic models, and agentic workflow enablement.

Required Qualifications, Capabilities, and Skills

·        8+ years of experience in enterprise data architecture, data modeling, semantic modeling, data warehousing, lakehouse architecture, or data product architecture.

·        Strong hands-on experience designing conceptual, logical, physical, canonical, dimensional, and semantic data models for enterprise analytics and operational consumption.

·        Deep knowledge of data lakehouse architecture, Bronze, Silver, and Gold patterns, data governance, metadata management, lineage, data quality, cataloging, business glossaries, and data product concepts.

·        Proven ability to define semantic layers, certified metrics, reusable business definitions, conformed dimensions, hierarchies, relationship models, and governed consumption structures.

·        Experience translating business requirements and domain knowledge into scalable, implementable, and well-documented data architecture and modeling deliverables.

·        Strong SQL, analytical modeling, documentation, stakeholder engagement, and communication skills, with the ability to work effectively across Agile, cross-functional delivery teams.

Preferred Qualifications, Capabilities, and Skills

·        Experience with Oracle Cloud Infrastructure and OCI-native data services such as OCI Data Catalog, Object Storage, GoldenGate, Autonomous Data Warehouse, Autonomous AI Lakehouse, or related Oracle AIDP capabilities.

·        Experience with modern lakehouse and open data technologies, including Apache Iceberg, Delta Lake, Parquet, ORC, or comparable storage and table formats.

·        Experience implementing Data Contracts, data product architecture, Data Mesh concepts, governed data marketplaces, metadata management, lineage, cataloging, and data product certification practices.

·        Healthcare payer experience across claims, provider, member, product, finance, or acquisition integration domains, particularly in environments requiring PHI protection, privacy, security, and governed data handling.

·        Exposure to AI-ready data architecture patterns supporting GenAI, RAG, semantic models, vector search, embeddings, knowledge graphs, ontologies, and agentic workflows.

·        Experience with enterprise architecture methods, governance operating models, AI-assisted modeling, DataOps, DevOps, observability concepts, technical documentation, and production support operating models.