AVP, Data Platform Engineering

The Hartford is an insurance company dedicated to making a difference and achieving goals. The AVP, Data Platform Engineering will provide strategic and technical leadership for enterprise data platforms, focusing on analytics capabilities, data integration services, and AI-enabled data solutions.

Responsibilities:

• Define and execute a multi-year strategy for enterprise data platforms, analytics enablement, AI-ready data capabilities, data integration services, Third-Party Data capabilities, and platform modernization aligned to business and technology priorities
• Serve as the senior technical leader for enterprise data platforms, providing architecture guidance, engineering direction, and technology decision-making across data, analytics, AI-enabled capabilities, and external data ecosystems
• Partner with Architecture, Product, AI & Analytics, Cybersecurity, Data Governance, Procurement, Risk, Legal, and business leaders to define platform roadmaps, service offerings, adoption plans, and measurable outcomes
• Lead organizational transformation initiatives that improve engineering maturity, operational effectiveness, delivery speed, and team capabilities
• Build, mentor, and develop high-performing engineering leaders and teams through coaching, talent development, succession planning, and organizational design
• Foster a culture of innovation, accountability, technical excellence, collaboration, and continuous improvement
• Serve as a trusted advisor to senior technology and business leaders on platform modernization, AI enablement, Third-Party Data capabilities, emerging technologies, and enterprise data investments
• Lead engineering teams responsible for enterprise data platforms and services, including Snowflake, Spark, Google BigQuery, Dataproc, Dataflow, Informatica IDMC, and related cloud-native data engineering capabilities
• Drive modernization of enterprise data platform capabilities through cloud adoption, platform rationalization, legacy migration, automation, and scalable engineering practices
• Establish engineering standards and best practices for platform architecture, data ingestion, orchestration, transformation, observability, reliability, performance optimization, security, automation, and cost management
• Improve engineering productivity through platform standardization, self-service capabilities, reusable engineering patterns, CI/CD adoption, infrastructure-as-code practices, and modern software engineering approaches
• Ensure enterprise data platforms are designed and operated for scalability, resilience, security, compliance, and operational excellence
• Lead the operationalization of new and evolving platform capabilities and services across the enterprise data ecosystem
• Enable enterprise analytics and business intelligence capabilities through platforms such as Tableau and ThoughtSpot, emphasizing trusted datasets, reusable data products, governed data access, semantic modeling, and self-service analytics
• Partner with analytics and business teams to deliver scalable, trusted, and business-aligned insights
• Advance next-generation analytics experiences including conversational analytics, Chat with Data, AI-assisted insight generation, embedded intelligence, and Agentic Analytics capabilities
• Drive modernization of analytics capabilities to improve data accessibility, business adoption, governance, performance, and user experience
• Lead the engineering strategy and platform capabilities that support AI-ready enterprise data platforms and services
• Build and scale foundational capabilities supporting semantic layers, ontology frameworks, knowledge graphs, contextual metadata, and trusted business definitions
• Partner with AI and analytics leaders to ensure enterprise platforms support emerging AI use cases and future AI initiatives
• Support integration of technologies and capabilities such as Snowflake Cortex, Gemini Enterprise integrations with BigQuery, vector search technologies, Retrieval-Augmented Generation (RAG) architectures, and AI/ML platform interoperability
• Ensure AI-enabling data capabilities are governed, scalable, secure, reusable, and aligned with enterprise architecture and governance standards
• Lead teams responsible for enterprise data integration, ingestion, API enablement, Third-Party Data services, and data movement capabilities across internal and external data ecosystems
• Serve as the executive leader for Third-Party Data platform capabilities, partnering with business stakeholders, data consumers, and strategic vendors to enable enterprise access to external data assets and services
• Establish and evolve modern data acquisition patterns, onboarding frameworks, integration standards, and underlying technologies that support scalable and secure Third-Party Data consumption across the enterprise
• Drive enterprise capabilities supporting external data onboarding, ingestion, governance, compliance, lineage, quality, metadata management, and operational support
• Partner with business leaders and strategic vendors to prioritize Third-Party Data initiatives and ensure platform capabilities align with enterprise data needs and business objectives
• Lead collaboration across technology, legal, risk, procurement, governance, and business stakeholders to ensure Third-Party Data solutions meet enterprise standards for compliance, security, and operational excellence
• Champion adoption of modern Third-Party Data capabilities, frameworks, and reusable integration patterns across the enterprise
• Translate stakeholder requirements into actionable platform, integration, and data engineering priorities
• Lead distributed engineering teams across multiple geographies and delivery models, including global partners and support organizations
• Influence teams and stakeholders across organizational boundaries, including groups that do not directly report into the organization
• Partner effectively with global engineering, support, and delivery organizations to drive alignment, accountability, and execution
• Establish operating models and delivery practices that support collaboration across employees, contractors, and global teams

Requirements:

• 12+ years of experience in data platform engineering, data architecture, cloud data platforms, analytics technologies, infrastructure engineering, or related disciplines, with a proven track record of leadership in complex enterprise environments
• Deep engineering leadership experience building, operating, and modernizing enterprise-scale data platforms, including Snowflake, Spark, Google BigQuery, Dataproc, Dataflow, Informatica IDMC, and comparable cloud-native technologies
• Strong understanding of data platform engineering practices, including platform architecture, ingestion, orchestration, transformation, observability, reliability, automation, performance tuning, cost management, security, and operational support
• Hands-on engineering background with the technical depth to guide architecture decisions, evaluate engineering tradeoffs, influence technology strategy, and provide credible leadership to architects and engineers
• Demonstrated success leading platform modernization, cloud transformation, engineering maturity improvements, and organizational change initiatives
• Experience leading enterprise Third-Party Data capabilities, including external data acquisition, vendor-enabled data services, onboarding frameworks, governance, compliance, and operational management
• Demonstrated success partnering with business stakeholders, strategic vendors, and cross-functional teams to deliver solutions leveraging external data assets and services
• Strong understanding of Third-Party Data lifecycle management, including acquisition, integration, governance, quality, lineage, compliance, risk management, and enterprise consumption patterns
• Experience with modern analytics and business intelligence platforms such as Tableau and ThoughtSpot, including self-service analytics, semantic modeling, dashboard modernization, and business user enablement
• Strong understanding of AI-enabled data ecosystems, including Snowflake Cortex, Gemini Enterprise integrations with BigQuery, conversational analytics, Chat with Data, Agentic Analytics, vector search technologies, AI/ML integration patterns, and Retrieval-Augmented Generation (RAG) architectures
• Experience supporting or implementing semantic layers, ontology-driven solutions, knowledge graphs, contextual metadata, metrics layers, and AI-ready enterprise knowledge models
• Exceptional strategic thinking, systems thinking, and problem-solving capabilities with the ability to balance near-term execution and long-term platform strategy
• Strong executive communication, presentation, and storytelling skills with the ability to explain complex technical concepts to both technical and non-technical audiences
• Proven ability to build, mentor, and develop high-performing teams while leading through organizational change and transformation
• Experience influencing stakeholders and driving results in highly matrixed organizations
• Experience with Google Cloud Platform and cloud-native data engineering ecosystems
• Experience leading Snowflake modernization, migration, optimization, or platform strategy initiatives
• Experience supporting semantic layer, ontology, knowledge graph, metadata, or AI-ready data platform initiatives
• Experience building or modernizing enterprise Third-Party Data platforms, acquisition frameworks, vendor data ecosystems, and external data enablement capabilities
• Experience with enterprise data integration, API enablement, Informatica, master data management, and large-scale data ecosystems
• Experience leading distributed or global engineering teams
• Strong knowledge of data governance, cybersecurity, privacy, compliance, data quality, lineage, metadata management, and risk management practices
• Experience within insurance, financial services, or other highly regulated industries
• Bachelor's degree in Computer Science, Data Science, Information Systems, Engineering, Business Administration, or a related quantitative field