AVP, Data Platform Engineering

The Hartford•Hartford, CT

1d•$177,600 - $266,400•Hybrid

About The Position

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.

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.

Nice To Haves

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.

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.