Oracle Health is seeking a Lead Semantic Layer Engineer to build the governed semantic and knowledge foundation that enables AI agents and end users to work from consistent business definitions, trusted metrics, and reusable analytics logic.
This role will own the design and governance of a semantic layer that captures business entities, relationships, metrics, dimensions, hierarchies, glossary terms, lineage, and validation rules across priority analytics domains. This semantic foundation will power AI-driven analytics use cases such as metric design, natural-language search, self-service reporting, dashboard generation, and executive narrative creation.
The ideal candidate combines strong analytics engineering and data modeling experience with a deep understanding of metric governance, business semantics, and how trusted definitions support both human and AI consumers. This role is central to ensuring that AI outputs are accurate, explainable, and aligned with Oracle Health business meaning.
Internal Responsibilities
- Design and implement a governed semantic layer for key analytics domains, including entities, relationships, dimensions, metrics, hierarchies, and business rules.
- Define certification and validation workflows for business metrics and semantic definitions.
- Build and maintain business glossary content, synonyms, reference logic, and reusable definitions that support AI search and natural-language interaction.
- Partner with analytics, reporting, and data teams to reconcile metric definitions and reduce duplicate or conflicting business logic.
- Enable AI and self-service reporting use cases by exposing trusted semantic definitions in machine-usable formats.
- Support AI workflows such as metric design, report generation, dashboard scaffolding, and narrative generation through high-quality semantic metadata.
- Establish documentation, lineage, versioning, and change-management practices for semantic assets.
- Help create the governed knowledge foundation required for accurate, explainable, enterprise-safe AI outputs.
External Responsibilities
- Design and implement a governed semantic layer for key analytics domains, including entities, relationships, dimensions, metrics, hierarchies, and business rules.
- Define certification and validation workflows for business metrics and semantic definitions.
- Build and maintain business glossary content, synonyms, reference logic, and reusable definitions that support AI search and natural-language interaction.
- Partner with analytics, reporting, and data teams to reconcile metric definitions and reduce duplicate or conflicting business logic.
- Enable AI and self-service reporting use cases by exposing trusted semantic definitions in machine-usable formats.
- Support AI workflows such as metric design, report generation, dashboard scaffolding, and narrative generation through high-quality semantic metadata.
- Establish documentation, lineage, versioning, and change-management practices for semantic assets.
- Help create the governed knowledge foundation required for accurate, explainable, enterprise-safe AI outputs.
