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VP Data Engineering, Business Insurance
The Hartford. Define and champion the Business Insurance AI Data Engineering strategy and operating model .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates mastery in Data Engineering and AI-ready data foundations, with a strong focus on enterprise data intelligence, semantic modeling, and advanced analytics. Proven ability to translate business strategies into actionable data engineering initiatives while ensuring operational performance and compliance.
Highest-signal resume keywords
Data Engineering StrategyEnterprise Data IntelligenceAdvanced Analytics LeadershipAI-Ready Data FoundationsData Governance Expertise
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
PythonSQLDistributed ProcessingSnowflakeSparkKafkaCloud-Native TechnologiesAPIsData ArchitectureData Modeling
Soft Skills
Executive CommunicationNegotiationRelationship ManagementConflict ResolutionStorytelling
Tools & Technologies
AWSGoogle CloudOrchestrationStreamingHybrid Cloud Environments
Industry Keywords
Property & CasualtyBusiness InsuranceData GovernanceResponsible AIRegulatory Compliance
Tech Stack
Tools & technologiesAWSCloudKafkaPythonSnowflakeSparkSQL
About the role
Key responsibilities & impact- Define and champion the Business Insurance AI Data Engineering strategy and operating model
- Translate business priorities into multi-year portfolio plans, capability roadmaps, workforce requirements, and measurable outcomes
- Set priorities and allocate resources across data products, modernization, AI-ready data foundations, technical debt, operational resilience, and emerging business needs
- Ensure delivery and ongoing operation of trusted, reusable, and scalable data products, pipelines, semantic assets, and data foundations
- Lead the Business Insurance agentic analytics strategy
- Build and scale enterprise data intelligence layers with semantics, metadata, ontologies, knowledge graphs, metrics, policies, lineage, and access controls
- Establish reusable semantic layers and governed business metrics across Business Insurance functions
- Partner with Analytics, AI, Architecture, Product, Governance, and business leaders
- Define operating models and controls for agentic data and analytics
- Connect Data Engineering capabilities to underwriting, pricing, distribution, servicing, claims, risk, customer, operational, and financial outcomes
- Guide structured and unstructured data capabilities, including embeddings, vector and graph technologies, retrieval and grounding data, and governed external data sources
- Establish expectations for data quality, testing, lineage, traceability, observability, reliability, lifecycle management, data contracts, service levels, and consumer accountability
- Champion responsible use of AI-assisted engineering
- Lead a large, complex organization through senior leaders
- Own portfolio delivery, operational performance, cost effectiveness, workforce outcomes, risk management, control maturity, audit readiness, and value realization
- Partner with business and technical executives to align priorities, architecture, platform capabilities, governance, dependencies, and outcomes
- Remove organizational and cross-functional barriers and guide major multidimensional initiatives
- Represent Business Insurance AI Data Engineering with senior leaders, governance bodies, strategic partners, and vendors while monitoring emerging data and AI trends
Requirements
What you’ll need- Bachelor's degree in Computer Science, Data Engineering, Information Systems, Engineering, Applied Mathematics, Business, or a related field; advanced degree preferred
- 18+ years of applicable experience in Data Engineering, data platforms, AI-ready data ecosystems, software engineering for data-intensive systems, technology leadership, or related disciplines
- 10+ years of progressive leadership experience, including leading senior leaders, large technical organizations, complex portfolios, or business-critical enterprise capabilities
- Demonstrated success translating business strategy into Data Engineering strategy, operating models, portfolio priorities, resource plans, workforce capabilities, and multi-year roadmaps
- Proven experience establishing enterprise data-product, semantic-layer, or data-intelligence capabilities
- Demonstrated leadership enabling advanced analytics, generative AI, RAG, or agentic analytics through governed, context-rich, traceable enterprise data foundations
- Experience partnering across Data Engineering, Analytics, AI, Architecture, Product, Governance, Risk, and business teams
- Proven experience managing significant budgets, workforce resources, strategic vendors, modernization priorities, and portfolio performance
- Strong business and financial acumen
- Meaningful experience advising executive leaders, influencing strategic decisions, navigating complex tradeoffs, and leading organizational change
- Exceptional executive communication, judgment, negotiation, relationship management, conflict resolution, presentation, and storytelling skills
- Significant Property & Casualty or Business Insurance experience strongly preferred
- Mastery of modern Data Engineering and AI-ready data foundations
- Deep expertise in enterprise data intelligence patterns
- Strong understanding of agentic analytics architectures and operating patterns
- Deep understanding of data foundations supporting generative AI, RAG, and agentic AI
- Demonstrated ability to design reusable semantic models and governed business metrics
- Strong understanding of enterprise data architecture and platform concepts
- Strong fluency in Python, SQL, distributed processing, Snowflake, Spark, Kafka, cloud-native technologies, orchestration, streaming, APIs, and hybrid or multi-cloud environments
- Experience designing and operating complex data ecosystems across AWS or Google Cloud
- Strong understanding of AI-assisted engineering and responsible adoption practices
- Deep understanding of Data Governance, Responsible AI, privacy, security, model and data risk, auditability, regulatory obligations, compliance, records management, and enterprise control frameworks
- Demonstrated ability to evaluate technical, modernization, vendor, resource, operational, and risk tradeoffs
Benefits
Comp & perks- Short-term or annual bonuses
- Long-term incentives
- On-the-spot recognition
- Perks & Benefits