AVP, Data Platform Engineering

About this role

AVP IT Engineering - IE05AE We’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals – and to help others accomplish theirs, too. Join our team as we help shape the future.   

As the AVP, Data Platform Engineering, you will provide strategic and technical leadership for enterprise data platforms, analytics capabilities, data integration services, AI-enabled data solutions, and Third-Party Data enablement across The Hartford.

This role is responsible for leading teams that build, modernize, and operate scalable, secure, and reliable data platforms that support analytics, business intelligence, AI capabilities, and enterprise consumption of internal and external data assets. The successful candidate will drive platform strategy, engineering excellence, operational effectiveness, technology transformation, and Third-Party Data capabilities while ensuring alignment to enterprise priorities and business objectives.

The ideal candidate combines strong engineering leadership, enterprise data platform expertise, and organizational transformation experience with the ability to influence technical and business stakeholders. This leader will play a critical role in advancing modern data capabilities, supporting enterprise AI initiatives, enabling enterprise adoption of Third-Party Data assets and services, and developing high-performing teams that deliver measurable business value.

Responsibilities

Engineering Leadership & Strategy • 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.

Data Platform Engineering • 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.

Analytics & Business Intelligence • 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.

AI-Ready Data Foundations • 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.

Data Integration & Third-Party Data Enablement • 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 cons

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