AVP, Data Platform Engineering

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 enterprise data, semantic, and knowledge platforms that support analytics, business intelligence, AI capabilities, graph-based knowledge architectures, and enterprise consumption of internal and external data assets.

The successful candidate will drive platform strategy, engineering excellence, operational effectiveness, technology transformation, semantic and knowledge platform capabilities, and Third-Party Data services while ensuring alignment to enterprise priorities and business objectives.

This leader will have accountability for the engineering, scalability, reliability, performance optimization, and operational management of enterprise graph databases, semantic layers, ontology-driven solutions, knowledge graphs, contextual metadata platforms, and AI-ready knowledge architectures that support analytics and AI initiatives across the enterprise.

The ideal candidate combines strong engineering leadership, enterprise data platform expertise, semantic technology 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, enabling enterprise AI initiatives, supporting 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, 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 BigQuery, 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, 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, semantic, and knowledge 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.

Semantic, Graph & Knowledge Platform Engineering

• Lead the strategy, engineering, modernization, and operational management of enterprise graph database platforms, semantic technologies, ontology services, knowledge graphs, and metadata-driven capabilities.

• Own the scalability, reliability, performance optimization, observability, security, and operational excellence of platforms supporting enterprise knowledge models and semantic architectures.

• Establish engineering standards and best practices for graph databases, semantic layers, ontology frameworks, metadata management, relationship modeling, entity resolution, and trusted business definitions.

• Lead teams responsible for designing, operating, and continuously improving semantic and knowledge platforms that support business intelligence, analytics, AI enablement, and enterprise data consumption.

• Partner with Architecture, Data Governance, AI, Analytics, and business stakeholders to define enterprise semantic strategies, knowledge models, metadata standards, and platform roadmaps.

• Drive adoption of graph-based and ontology-driven technologies to improve data discoverability, business context, knowledge management, and AI-readiness across the enterprise.

• Ensure enterprise semantic and knowledge platforms are engineered for scalability, governance, interoperability, compliance, and long-term sustainability.

• Evaluate and guide implementation of graph database technologies, semantic search capabilities, contextual metadata services, GraphRAG architectures, vector search technologies, and emerging knowledge platform capabilities.

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 and knowledge platforms.

• Build, scale, and operationalize enterprise semantic platforms supporting ontology frameworks, semantic layers, graph databases, knowledge graphs, contextual metadata services, business vocabularies, and trusted enterprise knowledge models.

• Lead engineering teams responsible for the performance, optimization, administration, scalability, governance, and operational management of graph databases and semantic technologies supporting AI-enabled enterprise capabilities.

• Define and evolve enterprise capabilities supporting semantic search, relationship modeling, entity resolution, knowledge retrieval, GraphRAG architectures, vector search, and AI-driven business discovery.

• 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, GraphRAG capabilities, and AI/ML platform interoperability.

• Ensure AI-enabling data capabilities are governed, scalable, secure, reusable, and aligned with enterprise architecture and governance standards.

Required Qualifications

• 12+ years of experience in data platform engineering, data architecture, semantic technologies, knowledge platforms, 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, semantic, and knowledge platforms.

• Strong understanding of data platform engineering practices, including platform architecture, ingestion, orchestration, transformation, observability, reliability, automation, performance tuning, cost management, security, and operational support.

• Strong engineering and platform leadership experience supporting graph databases, semantic technologies, ontology-driven solutions, knowledge graphs, metadata platforms, metrics layers, or related enterprise knowledge technologies.

• Demonstrated experience leading teams responsible for the architecture, scalability, optimization, reliability, and operational support of graph database platforms, semantic layers, ontology services, and AI-ready knowledge architectures.

• Deep understanding of semantic technologies, contextual metadata management, entity modeling, relationship modeling, business vocabularies, knowledge representation, and trusted business definitions.

• 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.

• Strong understanding of AI-enabled data ecosystems, including GraphRAG, vector databases, vector search technologies, Retrieval-Augmented Generation (RAG), Snowflake Cortex, Gemini Enterprise integrations, Agentic AI capabilities, and AI/ML integration patterns.

• 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.