AVP, Data Platform Engineering

AVP, Platform Engineer - IPB
05AE

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, semantic and knowledge 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 and knowledge platforms that enable analytics, AI, business intelligence, graph-based architectures, and enterprise consumption of internal and external data assets. The successful candidate will drive platform strategy, engineering excellence, technology transformation, operational effectiveness, and the advancement of semantic and knowledge capabilities that support enterprise priorities and business outcomes.

The ideal candidate combines deep engineering leadership experience with expertise in enterprise data platforms, graph databases, semantic layers, ontologies, knowledge graphs, and AI-ready data architectures. This leader will influence technical and business stakeholders, accelerate modern data and AI capabilities, expand Third-Party Data services, and develop high-performing teams that deliver measurable business value.

** Responsibilities*
* ** Engineering Leadership & Strategy*
* + Define and execute a multi-year strategy for enterprise data platforms, semantic and knowledge 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 and knowledge platforms, providing architecture guidance, engineering direction, and technology decision-making across data, analytics, AI-enabled capabilities, graph technologies, semantic platforms, and external data ecosystems.

+ Serve as the executive technology leader for enterprise semantic and knowledge platforms, including graph databases, semantic layers, ontology services, metadata platforms, and knowledge graph capabilities, establishing roadmaps, adoption strategies, and long-term investment plans.

+ 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, semantic technologies, AI enablement, Third-Party Data capabilities, emerging technologies, and enterprise data investments.

** Data Platform Engineering*
* + Lead engineering teams responsible for enterprise data, semantic, and knowledge platforms, including Snowflake, Spark, Google Big Query, Dataproc, Dataflow, graph databases, metadata platforms, Informatica IDMC, and related cloud-native engineering capabilities.

+ Drive modernization of enterprise data and knowledge platform capabilities through cloud adoption, platform rationalization, legacy migration, automation, and scalable engineering practices.

+ Own the engineering lifecycle of graph database platforms, semantic layers, ontology services, and knowledge graph technologies, including architecture, scalability, performance optimization, reliability engineering, observability, security, and operational support.

+ Establish engineering standards and operating models for graph databases, semantic technologies, metadata services, business vocabularies, entity relationship management, and AI-ready information architectures.

+ Establish engineering standards and best practices for platform architecture, data ingestion, orchestration, transformation, observability, reliability, performance optimization, security,…