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# Principal Data Engineer - Semantic Layer

- **Company:** MetLife
- **Location:** Bridgewater, NJ, United States
- **Experience:** Expert
- **Salary:** $130,000.0 - $189,400.0
- **Contract:** Permanent contract
- **Skills:** Query Performance, Artificial Intelligence, Data Analysis, Apache HTTP Server, Microsoft Azure, Encodings, Data Architecture, Information Engineering, Data Governance, Extract Transform Load (ETL), Serialization, Dataspaces, Data Warehousing, Query Languages, Software Design Patterns, Distributed Data Store, Distributed Systems, Graph Database, IBM Cognos Business Intelligence, Interoperability, Java Database Connectivity, JSON, Python (Programming Language), Linked Data, Metadata, Metadata Standards, NoSQL, Open Database Connectivity, Web Ontology Language, Power BI, SPARQL, SQL Databases, Systems Integration, Extensible Markup Language (XML), Enterprise Data Management, Large Language Models, Snowflake, Apache Spark, Generative AI, Knowledge Representation, Microsoft Fabric, Data Lakes, Data Lineage, Collibra, Data Analytics, Non-relational Database, Graphql, Data Management, Tools for Reporting, Azure Synapse Analytics, Data Pipelines, Databricks
- **Published:** July 9, 2026
- **Apply:** https://dejobs.org/x/x/D7C9B18736004954BED4E07FAB3C049C/job/

## About the Role

* 10+ years of experience in data engineering, data architecture, or related disciplines, with at least 3+ years of hands-on experience in Semantic Layer implementation or ontology engineering.
 * Demonstrated experience deploying enterprise Semantic Layer platforms (ex: AtScale, Cube.dev, dbt Semantic Layer, etc.) in production environments.
 * Exposure or knowledge of ontologies and ontology design.
 * Deep expertise in most of the following modern cloud data platforms and tools: Azure Databricks, Azure Synapse, Microsoft Fabric, or Snowflake; proficiency in SQL, NoSQL, Python, and Spark.
 * Bachelor's Degree in a relevant field., * Experience with knowledge graph platforms and graph query languages (Cypher, SPARQL) in enterprise contexts.
 * Exposure to GenAI, LLMs, and RAG architectures that consume structured ontologies and semantic models.
 * Familiarity with FAIR data principles (Findable, Accessible, Interoperable, Reusable) and open linked data standards.
 * Experience with financial services semantic standards such as FIBO (Financial Industry Business Ontology).
 * Relevant certifications: Microsoft Certified Azure Data Engineer Associate, Databricks Certified Data Engineer Professional, or equivalent.
 * Prior experience in large-scale insurance or financial services enterprise data programs.
 * Proven experience designing formal ontologies using OWL 2 (DL and RL profiles) published in OWL/XML, RDF/XML, and JSON-LD serialization formats.
 * Hands-on experience with SPARQL 1.1, OWL reasoning engines (HermiT, Pellet, or RDFox), and triple stores or graph databases (Apache Jena, Stardog, GraphDB, or Amazon Neptune).

Location Expectation: This is a hybrid role requiring a minimum of 3 days per week in office.

## Description

The Principal Data Engineer - Semantic Layer is a senior technical role responsible for designing, building, and governing MetLife's enterprise Semantic Layer. This engineer will serve as the primary practitioner translating complex, distributed data assets into well-structured, business-consumable ontologies and semantic models. Leveraging OWL (Web Ontology Language), RDF, SPARQL, and modern Semantic Layer platforms, the engineer will create a unified, trustworthy representation of enterprise data that powers analytics, AI/ML, and regulatory reporting across the organization. This role sits at the intersection of data engineering, knowledge engineering, and enterprise architecture. The successful candidate will partner with data governance, platform engineering, business stakeholders, and the Enterprise Metadata team to embed semantic interoperability into MetLife's data ecosystem., Semantic Layer Design & Implementation

 * Architect and implement an enterprise-grade Semantic Layer using platforms such as Stardog, Timbr.ai, AtScale, dbt Semantic Layer, or Databricks/ Microsoft Fabric Semantic Models, establishing a single source of truth for business metrics and dimensions.
 * Design and publish OWL 2 ontologies and RDF-based data models that encode business concepts, relationships, and constraints as formal, machine-readable knowledge graphs.
 * Develop and maintain semantic models in OWL/XML, RDF/XML, and JSON-LD serialization formats, ensuring alignment with W3C, Dublin Core, SKOS, and PROV-O standards.
 * Define and govern reusable business metrics, KPIs, and calculated measures within the Semantic Layer to ensure consistent, governed consumption across BI, reporting, and AI use cases.
 * Build and maintain SPARQL query templates and OWL reasoning rules that enable downstream consumers to query semantic models efficiently.

Platform Integration & Data Engineering

 * Integrate the Semantic Layer platform with enterprise data platforms including Azure Databricks, Azure Synapse Analytics, Microsoft Fabric, and Delta Lake, enabling high-performance federated query execution.
 * Develop and maintain ETL/ELT pipelines that populate and synchronize semantic models with upstream source systems, data lakes, and data warehouses.
 * Enable direct connectivity between the Semantic Layer and BI/analytics tools (Power BI, Cognos Analytics) via JDBC/ODBC, MDX, and DAX interfaces.
 * Design and expose REST and GraphQL APIs that provide downstream applications, AI agents, and self-service analytics platforms with structured access to semantic models.
 * Support LLM-based systems by providing structured ontology exports consumable by retrieval-augmented generation (RAG) pipelines and AI co-pilots.

Enterprise Metadata & Data Governance

 * Partner with the Enterprise Metadata team to align semantic model definitions with the enterprise data catalog (Collibra, or equivalent), ensuring bidirectional metadata traceability.
 * Implement and enforce metadata standards including business glossary linkage, data lineage tagging, and classification schemes across all semantic assets.
 * Embed data quality validation rules and data contract specifications within the Semantic Layer to guarantee fitness-for-use at point of consumption.
 * Support data governance initiatives including HIPAA compliance, data residency, and access control enforcement through semantic model-level row and column security.
 * Maintain a governed registry of semantic model versions enabling auditability and rollback in regulated environments.

Collaboration, Enablement & Thought Leadership

 * Act as the primary technical authority on semantic platform management, data modeling, ontology engineering, and knowledge representation within MetLife's Data & Analytics organization.
 * Collaborate with data product teams, domain architects, and business analysts to capture domain knowledge and encode it as formal OWL ontologies.
 * Define and publish Semantic Layer standards, design patterns, and reference implementations for adoption across business domains.
 * Mentor data engineers and analytics engineers on OWL/RDF tooling, SPARQL, and Semantic Layer platform best practices.
 * Evaluate and recommend emerging Semantic Layer and knowledge graph technologies through structured proofs-of-concept and capability assessments
 * Collaborate with engineering and architecture teams to support delivery across Relational and non-relational databases (e.g., SQL, NoSQL)
 * Identify and proactively manage challenges related to:
 * Data latency and consistency
 * Query performance and optimization Integration
 * complexity across distributed systems

Resources Management

 * Coordinate and manage external consultants and vendor partners
 * Ensure alignment of external teams with internal priorities, delivery standards, and timelines.

Stakeholder Communication & Executive Reporting

 * Prepare and deliver clear, concise executive-level updates.
 * Provide visibility into:
 * Program progress.
 * Key risks and mitigation plans.
 * Decision points and dependencies.


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