Business Systems Analyst - Semantic Layer

Description and Requirements The Team You Will Join

At Met Life, data isn't just a tool - it is a catalyst for growth. As part of our Data & Analytics organization, you'll unlock trusted insights that drive bold decisions, power personalized customer experiences, and deliver lasting business impact. Your skills in AI, predictive modeling, and advanced analytics will help solve complex challenges, scale solutions globally, and fuel innovation across every region.

We're building the future of data - one that's governed responsibly, engineered for scalability, and designed for growth. When you join us, you're not just supporting the business - you're empowering it. Let's transform insight into impact and data into action, together.

As a Business Systems Analyst for the Semantic Layer program, you will serve as the critical bridge between Met Life’s insurance business domains and the technical teams building the enterprise semantic layer. You will translate complex insurance concepts into formally structured ontologies and business metadata, enabling consistent, governed, and reusable data definitions across the organization. This is a high-impact, hands‑on role for a practitioner who combines deep insurance industry knowledge with expertise in ontology design, business metadata management, and semantic technologies.

You will help define how Met Life speaks data & create the common language that connects business users, data engineers, AI models, and analytics consumers.

Semantic Layer Design & Implementation

• Lead requirements gathering and functional design for Met Life enterprise semantic layer, translating business concepts into structured semantic models.
• Define and maintain the business glossary, logical data models, and semantic mappings that align insurance terminology across business units and systems.
• Collaborate with data architects and engineers to implement semantic layer components in platforms such as Stardog, Dremio, AtScale Semantic Layer or equivalent tools.
• Design metric definitions, KPI hierarchies, and dimensional models that reflect insurance business logic (e.g., earned premium, incurred loss, persistency, claim frequency).

Ontology & Business Metadata Implementation

• Design and implement insurance-domain ontologies using standards such as OWL, RDF, SKOS, or industry frameworks (e.g., ACORD, OMG Insurance Domain Analysis Model).
• Develop and maintain the enterprise business metadata catalog — defining data assets, ownership, lineage, classifications, and business definitions within tools such as Collibra, Alation, or Apache Atlas.
• Establish and enforce metadata governance standards: term definitions, synonym management, hierarchical classifications, and cross-domain concept alignment.
• Map physical data assets (tables, columns, APIs) to business concepts and ontological entities, enabling semantic search and automated data discovery.
• Partner with Data Governance and stewardship teams to ensure ontology and metadata artifacts meet regulatory, compliance, and audit requirements.

Insurance Domain Analysis

• Apply deep understanding of insurance product lines — Group Benefits, Life, Disability, Dental, Vision, and Voluntary — to accurately model business entities, events, and relationships.
• Translate insurance operational concepts (policy lifecycle, claims adjudication, underwriting, billing, reinsurance, regulatory reporting) into precise semantic definitions.
• Identify cross-LOB data harmonization opportunities by standardizing entities such as Employer, Policies, Claims and Broker, and Pricing across disparate source systems.
• Support regulatory and statutory reporting requirements by ensuring semantic models align with NAIC, GAAP, and IFRS 17 data constructs.

Stakeholder Collaboration & Delivery

• Work directly with business owners, data stewards, analytics engineers, and AI/ML teams to gather requirements and validate semantic model designs.
• Collaborate with engineering and architecture teams to support delivery across:
  • Relational and non-relational databases (e.g., SQL, No
    
    SQL).
  • Multi-database environments and cross-platform data integration.
• Facilitate workshops and…