AVP, Data Platform Engineering

AVP, Platform Engineer - IPB5AE

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As the AVP Data Platform Engineering, you will be a senior engineering leader responsible for defining, building, and operating enterprise-grade data platforms, analytics capabilities, and third-party data ingestion services that enable scalable, reliable, secure, and cost-effective data and AI outcomes across HIG. This role will lead engineering teams accountable for modern cloud data platforms such as Snowflake, Google BigQuery, Dataproc, Dataflow, and Informatica IDMC, while also enabling enterprise insights and self-service analytics capabilities through business intelligence platforms.

The leader in this role will be responsible for driving strong engineering practices, platform modernization, developer productivity, operational reliability, and product-oriented delivery for data platform services. The role requires a strong understanding of how AI capabilities integrate into modern data platforms, including Snowflake Cortex, GCP Gemini Enterprise integrations with BigQuery, conversational analytics, Chat with Data, and emerging Agentic Analytics patterns. This leader will also provide the foundations of enterprise capabilities around semantic layers, ontology, knowledge graphs, and related platforms that enable trusted, contextual, reusable, and business-aligned analytics experiences.

In addition, this role will provide leadership for Third Party Data strategy, including vendor data acquisition, data/API ingestion capability development, platform integration, business stakeholder engagement, and delivery of external data capabilities that meet HIG business requirements. The role requires close partnership across Enterprise Data Services, AI & Analytics, Architecture, Cybersecurity, Data Governance, business stakeholders, and external vendors to ensure that platform and third-party data solutions are secure, governed, scalable, and aligned to enterprise priorities.

 

Responsibilities:

• Define and execute a multi-year engineering strategy for enterprise data platforms, BI enablement, self-service analytics, AI-enabled analytics, and Third Party Data capabilities aligned with HIG business priorities.
• Lead engineering teams responsible for modern data platforms and services, including Snowflake, Spark, Google BigQuery, Dataproc, Dataflow, Informatica IDMC, and related cloud-native data engineering capabilities.
• Improve engineering productivity by simplifying developer experience, reducing platform friction, accelerating delivery, and enabling repeatable, high-quality data product development.
• Partner with architecture and product leaders to define data platform roadmaps, platform product strategy, service offerings, adoption plans, and measurable outcomes.
• Lead modernization of data platform and analytics capabilities, including migration from legacy platforms, rationalization of tools, and adoption of scalable cloud-native patterns.
• Enable enterprise BI and analytics capabilities through platforms such as Tableau and ThoughtSpot, with emphasis on trusted datasets, self-service analytics, governed data access, and reusable data products.
• Advance next-generation analytics experiences including Chat with Data, conversational BI, Agentic Analytics, AI-assisted insight generation, and embedded intelligence across analytics workflows.
• Develop a practical AI integration strategy for data platforms, including capabilities such as Snowflake Cortex, GCP Gemini Enterprise integrations with BigQuery, vector search, retrieval patterns, and governed AI/ML platform interoperability.
• Build, mentor, and lead high-performing engineering teams with strong technical depth, delivery accountability, inclusive leadership, and a continuous improvement mindset.
• Act as a strategic advisor to senior leadership on data platform modernization, analytics enablement, AI integration opportunities, third-party data strategy, and enterprise data capability investments.
• Lead Third Party Data strategy, including external data sourcing, vendor data integration patterns, acquisition roadmaps, consumption models, governance, usage optimization, and business value realization.
• Build and mature API and data ingestion capabilities for third-party vendor data, ensuring solutions meet HIG stakeholder requirements for timeliness, quality, reliability, security, lineage, and scalability.
• Manage third-party data vendor relationships in partnership with business, sourcing, legal, risk, and compliance teams, including service expectations, data quality, roadmap alignment, and performance management.
• Translate HIG business stakeholder needs into actionable platform, data ingestion, analytics, and third-party data requirements; prioritize delivery against enterprise value, risk, and feasibility.
  

Required Qualifications:

• Minimum of 12+ years of experience in data platform engineering, data architecture, data analytics, business intelligence, data warehousing, cloud data platforms, or related disciplines, with a proven track record of leadership in complex enterprise environments.
• Deep engineering leadership experience building and operating enterprise-scale data platforms, preferably including Snowflake, Spark, Google BigQuery, Dataproc, Dataflow, Informatica IDMC, and similar cloud-native data platform services.
• Strong understanding of data platform engineering practices, including platform architecture, data ingestion, orchestration, transformation, observability, performance tuning, reliability, automation, cost management, security, and operational support.
• Technical experience with LLMs, AI platforms, prompt engineering, LLM optimization, Retrieval-Augmented Generation (RAG) architectures and vector database technologies (Vertex AI, Postgres, OpenSearch, Pinecone etc.)
• Demonstrated experience leading engineering productivity improvements, platform standardization, reusable frameworks, CI/CD adoption, infrastructure-as-code patterns, and modern software/data engineering practices.
• Experience with modern BI and analytics platforms such as Tableau and ThoughtSpot, including self-service analytics, governed analytics, semantic modeling, dashboard modernization, and business user enablement.
• Strong understanding of AI-enabled data and analytics capabilities, including Snowflake Cortex, GCP Gemini Enterprise integrations with BigQuery, conversational analytics, Chat with Data, Agentic Analytics, vector search, and AI/ML integration patterns.
• Expertise implementing or governing semantic layers, metrics layers, ontology-driven solutions, knowledge graphs, graph databases, contextual metadata, and platforms that support trusted business definitions and AI-ready enterprise knowledge models.
• Experience leading the engineering, operation, optimization, and scaling of enterprise semantic, ontology, and knowledge graph platforms, leveraging technologies such as Neo4j, Amazon Neptune, Stardog, RDF, OWL, SPARQL, or comparable graph and semantic technologies.
• Exceptional strategic thinking, problem-solving, systems thinking, and critical-thinking abilities, with the ability to balance near-term delivery with long-term platform strategy.
• Superior communication, executive presentation, and storytelling skills, with the ability to explain complex data platform, AI, analytics, and third-party data concepts to technical and non-technical audiences, including senior executives.
• Strong team leadership, mentoring, talent development, organizational change management, and cross-functional partnership skills.

Additional experience:

• Experience developing third-party data strategies, vendor data integration approaches, external data acquisition models, API/data ingestion capabilities, and business-aligned data consumption patterns.
• Experience with product management or platform product leadership in an enterprise setting, including roadmap development, prioritization, stakeholder engagement, OKRs, financial management, and adoption measurement.
• Strong knowledge of data governance, cybersecurity, privacy, compliance, data quality, lineage, metadata management, and risk management considerations for enterprise data platforms and third-party data usage.
• Expertise in SQL, Python, or other relevant scripting and engineering languages for data engineering, automation, analysis, and platform integration.
• A bachelor's degree in Computer Science, Data Science, Information Systems, Engineering, Business Administration, or a related quantitative field.
  

​Location Requirements:

This role can have a Hybrid or Remote work arrangement. Candidates who live near our Hartford, CT or Charlotte offices will have the expectation of working in an office 3 days a week (Tuesday through Thursday). Candidates who do not live near an office should maintain their current work arrangement with the expectation of coming into the office as business needs arise. This position is not eligible for employment visa sponsorship. Candidates must be legally authorized to work in the United States without the need for current or future employer sponsorship.

Compensation

The listed annualized base pay range is primarily based on analysis of similar positions in the external market. Actual base pay could vary and may be above or below the listed range based on factors including but not limited to performance, proficiency and demonstration of competencies required for the role. The base pay is just one component of The Hartford’s total compensation package for employees. Other rewards may include short-term or annual bonuses, long-term incentives, and on-the-spot recognition. The annualized base pay range for this role is:

$201,500.00 - $302,500.00

Equal Opportunity Employer/Sex/Race/Color/Veterans/Disability/Sexual Orientation/Gender Identity or Expression/Religion/Age

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