AVP Data Engineering – Business Insurance Ontology, Knowledge Graph

🔥 0 minutes ago

🦌 Connecticut, Massachusetts – Remote

🦌 Connecticut – Remote 🍂 Massachusetts – Remote

💵 $182k - $273k / year

⏰ Full Time

🔴 Lead

🚰 Data Engineer

🦅 H1B Visa Sponsor

This company has sponsored H1B visas in the past.

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The Hartford

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1810

🛡️ Insurance

💸 Finance

👥 B2C

Insurance • Finance • B2C

The Hartford is a well-established insurance company offering a variety of products including auto, home, and business insurance. With over 200 years of experience, it caters to both individuals and small to midsize businesses, providing tailored insurance solutions and employee benefits. The company is particularly known for its AARP-endorsed auto and home insurance programs, which offer exclusive benefits and discounts for members.

📋 Description

• Serve as the primary data and semantic advisor to the heads of Small Business, Middle & Large Business, and Global Specialty • Present strategy, roadmap, execution progress, value realized, risks, and decision requests to executive leadership, steering committees, and ELT forums • 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 with Technology, Enterprise Architecture, Data & AI COE, and Applied AI teams • Negotiate priorities, resolve semantic and ownership conflicts, and broker alignment on enterprise definitions • Act as an internal and external 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 enterprise-scale semantic layer design and execution • Define and operationalize ontologies for commercial insurance concepts and relationships • Establish canonical vocabularies, metric definitions, business rules, and taxonomies • Define and enforce standards for ontology modeling, graph modeling, semantic interoperability, versioning, and ontology reuse • Embed semantic models into data products, analytics, AI pipelines, APIs, and activation layers; measure adoption and business value • Move capabilities from strategy to pilot to scaled enterprise production for high-value use cases • Enable semantic-first AI and agentic analytics using governed business context, metrics, rules, and relationships • Lead AI data pipelines integrating structured, semi-structured, and unstructured insurance data • Operationalize ontologies through knowledge graphs, metadata services, data APIs, vector stores, RAG pipelines, and production data platforms • Design and oversee scalable real-time and batch data pipelines • Drive production-scale execution of semantic and graph platforms with performance, reliability, security, and lifecycle standards • Ensure AI and analytical outputs are traceable to governed definitions, relationships, rules, and source data • Champion AI-augmented productivity across the data lifecycle • Evaluate emerging agentic AI, knowledge graph, and data engineering technologies; lead proofs of value and pilots • Align ontologies and graph models with data governance, stewardship, metadata, lineage, quality, privacy, and security standards • Build, mentor, and lead a team of ontologists, graph modelers, semantic and AI data engineers, business data analysts, and release train engineers • Manage portfolio budget, vendor partners, and financials; deliver measurable ROI • Operate within Scaled Agile and portfolio/value stream frameworks • 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 • Working knowledge of commercial lines and products including Workers' Compensation, General Liability, Commercial Property, Commercial Auto, Business Owner's Policy, Umbrella/Excess, Management and Professional Liability, and specialty lines • Understanding of differences among Small Business, Middle & Large Business, and Global Specialty • Familiarity with insurance concepts and measures including exposures, rating bases, class codes, insured locations, account rounding, hit/quote/bind ratios, written and earned premium, loss ratio, combined ratio, retention, and rate change • Familiarity with ACORD, ISO, and NCCI • Exceptional executive presence and ability to communicate complex technical concepts to senior audiences • Strong executive influence, negotiation, and conflict resolution skills • Proven ability to build cross-functional coalitions and drive enterprise-wide adoption without direct authority • Ability to frame investment cases, value metrics, and decisions for senior leadership • Ability to lead in a lean, agile environment using Scaled Agile principles • Deep expertise in semantic layer architecture, ontology modeling, graph modeling, and enterprise knowledge graph design • Hands-on mastery of RDF, OWL 2, RDFS, SKOS, SHACL, SPARQL, and W3C semantic web standards • Familiarity with labeled property graphs and Cypher or GQL • Experience with Neo4j, Amazon Neptune, Stardog, or Spanner Graph • Experience integrating knowledge graphs with LLMs, RAG, GraphRAG, vector stores, and agentic frameworks • Strong understanding of context-aware data engineering, canonical modeling, semantic interoperability, 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 with scalable AI-driven data systems, including AWS Lambda, S3, EC2, LangChain, LangGraph, MCP, and A2A • Technical expertise in LLMs, prompt engineering, LLM optimization, RAG architectures, and vector databases • Hands-on experience with 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 related field, or equivalent experience • 12+ years in data engineering, data architecture, and large-scale data ecosystems • 8+ years of hands-on semantic, ontology, or knowledge graph work • 7+ years in senior leadership roles managing complex data and AI portfolios • 5+ years in commercial Property & Casualty insurance • CPCU, AINS, or AU are a plus

🏖️ Benefits

• Short-term or annual bonuses • Long-term incentives • On-the-spot recognition • Perks & Benefits

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