AVP Data Engineering – Business Insurance Ontology & Knowledge Graph

The Hartford is hiring an AVP Data Engineering leader to define, build, and drive enterprise adoption of its Business Insurance ontology, semantic layer, and knowledge graph. The role leads teams and partners with business and technology executives to enable trusted analytics, AI, agentic experiences, and decision automation across the company’s insurance markets.

Responsibilities:

• Serve as the primary data and semantic advisor to the heads of Small Business, Middle & Large Business, and Global Specialty, translating ontology and knowledge graph capabilities into business outcomes, investment cases, and decisions
• Present strategy, roadmap, execution progress, value realized, risks, and decision asks to executive leadership, steering committees, and ELT forums in clear, plain business language
• Build executive sponsorship and drive adoption of a shared Business Insurance vocabulary across underwriting, distribution, product, claims, actuarial, finance, and operations
• Influence enterprise technology, data, platform, and AI strategy in partnership with Technology, Enterprise Architecture, Data & AI COE, and Applied AI teams
• Negotiate priorities, resolve semantic and ownership conflicts across lines of business, and broker alignment on enterprise definitions
• Act as an external and internal thought leader on ontology, knowledge graphs, agentic analytics, and AI-native data engineering in insurance
• Own the vision, strategy, and multi-year roadmap for the Business Insurance ontology, semantic layer, context graph, and knowledge graph
• Lead the design and execution of an enterprise-scale semantic layer that standardizes business meaning and enables trusted analytics, AI, and agentic use cases
• Define and operationalize ontologies modeling core commercial insurance concepts and relationships: customers, business profiles, accounts, submissions, quotes, policies, coverages, products, exposures, risks and locations, claims, producers, and portfolio performance
• Establish canonical vocabularies, metric definitions, business rules, and taxonomies that standardize meaning across structured and unstructured data sources and across all three markets
• Define and enforce standards and reusable patterns for ontology modeling, graph modeling, semantic interoperability, versioning, and ontology reuse across delivery teams
• Drive adoption by embedding semantic models into data products, analytics, AI pipelines, APIs, and activation layers; measure and report adoption and business value
• Move capabilities from strategy to pilot to scaled enterprise production, prioritizing high-value use cases such as submission triage, risk appetite, clearance, pricing and underwriting insights, portfolio management, and cross-sell
• Enable semantic-first AI and agentic analytics, ensuring LLMs and agents consume governed business context, metrics, rules, and relationships
• Lead implementation of AI data pipelines integrating structured, semi-structured, and unstructured data (e.g., submissions, loss runs, schedules, broker correspondence) to support AI and agentic solutions
• Operationalize ontologies through knowledge graphs, metadata services, data APIs, vector stores, RAG pipelines, and production data platforms in partnership with AI Data Architects and Engineers
• Design and oversee scalable real-time and batch data pipelines for efficient ingestion, processing, and delivery
• Drive production-scale execution of semantic and graph platforms with strong standards for performance, reliability, security, and lifecycle management
• Ensure explainability by making AI and analytical outputs traceable to governed semantic definitions, relationships, rules, and source data
• Champion AI-augmented productivity across the data lifecycle, including AI-generated pipelines, automated data quality, DataOps, and AI-assisted ontology engineering
• Evaluate emerging technologies in agentic AI, knowledge graphs, and data engineering; lead proofs of value and pilots to accelerate speed to market
• Align ontologies and graph models with enterprise data governance, stewardship, metadata, lineage, quality, privacy, and security standards; establish semantic ownership and change control with business data owners
• Build, mentor, and lead a high-performing team of ontologists, graph modelers, semantic and AI data engineers, business data analysts, and release train engineers, including onshore and offshore talent
• Manage the portfolio budget, vendor partners, and financials; deliver measurable return on investment
• Operate within Scaled Agile and portfolio/value stream frameworks, partnering closely with product owners and cross-functional delivery teams
• Evangelize agentic data engineering and semantic practices through communities of practice, reference patterns, playbooks, and enablement materials

Requirements:

• Must be eligible to work in the US without company sponsorship
• Deep understanding of the commercial P&C value chain: distribution and producer management, submission intake, clearance, risk appetite, underwriting, rating and pricing, quote and bind, policy administration, endorsements, renewals, billing, claims, and reinsurance
• Working knowledge of commercial lines and products such as Workers' Compensation, General Liability, Commercial Property, Commercial Auto, Business Owner's Policy, Umbrella/Excess, Management and Professional Liability, and specialty lines
• Understanding of how Small Business, Middle & Large Business, and Global Specialty differ in distribution model, underwriting approach, account complexity, and data needs
• Familiarity with core insurance concepts and measures: exposures and rating bases (payroll, sales, TIV, vehicles), class codes (NAICS/SIC), insured locations, account rounding, hit/quote/bind ratios, written and earned premium, loss ratio, combined ratio, retention, and rate change
• Familiarity with industry data standards and reference models such as ACORD, ISO, and NCCI
• Exceptional executive presence with proven ability to communicate complex technical concepts simply and persuasively to C-suite, line-of-business heads, and board-level audiences
• Strong executive influence, negotiation, and conflict resolution skills; track record of building cross-functional coalitions and driving enterprise-wide adoption without direct authority
• Proven ability to frame investment cases, value metrics, and decisions for senior leadership
• Ability to lead in a lean, agile, fast-paced environment using Scaled Agile principles
• Deep expertise in semantic layer architecture, ontology modeling, graph modeling, and knowledge graph design at enterprise scale
• Hands-on mastery of RDF, OWL 2, RDFS, SKOS, SHACL, SPARQL, and W3C semantic web standards; familiarity with labeled property graphs and query languages such as Cypher or GQL
• Experience designing and operating enterprise knowledge graphs on platforms such as Neo4j, Amazon Neptune, Stardog, or Spanner Graph
• Experience integrating knowledge graphs with LLMs, RAG and GraphRAG pipelines, vector stores, and agentic frameworks
• Strong understanding of context-aware data engineering, canonical modeling, semantic interoperability, and AI consumption patterns, including embeddings, grounding, reasoning, and explainability
• Mastery of data engineering and architecture, including lakehouses, data integration, data domains, data products, data APIs, real-time streaming, and conversational BI
• Experience implementing scalable AI-driven data systems supporting agentic solutions (e.g., AWS Lambda, S3, EC2, LangChain, LangGraph, MCP, A2A)
• Technical expertise in LLMs, prompt engineering, LLM optimization, RAG architectures, and vector databases (e.g., Vertex AI, Postgres, OpenSearch, Pinecone)
• Hands-on experience with multi-cloud environments across AWS, GCP, and Snowflake
• Strong grounding in data governance, metadata management, lineage, data quality, privacy, and security
• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Information Management, or a related field, or equivalent experience
• 12+ years in data engineering, data architecture, and large-scale data ecosystems, including 8+ years of hands-on semantic, ontology, or knowledge graph work
• 7+ years in senior leadership roles managing complex data and AI portfolios, with direct engagement with executive and line-of-business leadership
• 5+ years in commercial Property & Casualty insurance
• experience across Small Business, Middle Market, or Specialty strongly preferred
• Industry designations such as CPCU, AINS, or AU are a plus