Director Data and Analytics AI Engineering

The Hartford•Hartford, CT

1d•$156,000 - $234,000•Hybrid

About The Position

The Hartford is seeking a Director of AI Data Engineering. The Actuarial Solutions Engineering team develops and maintains cloud-based data and analytics solutions that help actuaries deliver faster, deeper, and more actionable insights for pricing, reserving, underwriting, portfolio management, and business decision making. This role leads the delivery of governed next generation actuarial data products, semantic layers, analytic-ready datasets, AI analytic agents, dashboards, and reusable solutions designed to enable insights. Working across actuarial, analytics, data science, IT, and business teams, the leader translates use cases into scalable, production-ready Snowflake solutions and oversees data discovery, modeling, transformation, validation, governance, monitoring, and support. The role manages a complex technical team, sets priorities, resolves delivery and resource challenges, and provides expert guidance on cloud data engineering, actuarial analytics, and AI-enabled solutions. A deep understanding of technical data capabilities coupled with a good understanding of data insights is critical to this role.

Requirements

8-10 years of experience in data engineering, analytics solution delivery, actuarial analytics, or related data-intensive roles.
3+ years' experience supporting actuarial, insurance, pricing, reserving, modeling, underwriting, portfolio management, or financial analytics use cases.
Mastery level data engineering and architecture skills, including deep expertise in data architecture patterns, data warehouse, data integration, data lakes, data domains, data products, business intelligence, and cloud technology capabilities along with data governance
Technical expertise in LLMs, AI platforms, prompt engineering, LLM optimization, Retrieval-Augmented Generation (RAG) architectures and vector database technologies (Vertex AI, Postgres, OpenSearch, Pinecone etc.).
Experience enabling AI for analytics use cases, including verified analytic agents, natural language analytics, governed knowledge bases, forecasting, monitoring, deep-dive analysis, and decision-support solutions for actuaries.
Ability to design, implement, and oversee Snowflake-based data pipelines, transformations, validations, and analytic consumption patterns.
Proven experience building modern actuarial data products, including governed consumption layers, semantic layers, reusable metrics, data quality controls, and analytics-ready models
Proven ability to use Snowflake-native and cloud-integrated capabilities to support actuarial workflows, predictive model outputs, monitoring, dashboards, and AI-enabled analytics solutions.
Ability to promote exploratory actuarial solutions into sustainable, production-ready assets with clear ownership, monitoring, documentation, and support expectations.
Ability to partner with actuaries, data scientists, analytics teams, engineering teams, and business stakeholders to translate actuarial needs into scalable Snowflake data products and AI-enabled analytics solutions.

Nice To Haves

Strong understanding of actuarial measures and analytical concepts such as loss ratio, frequency, severity, rate adequacy, modeled indications, emergence monitoring, renewal health, and book assessment preferred.

Responsibilities

Expertise in data engineering practices, knowledge of AI technologies, and the ability to lead cross-functional teams. Expertise in real-time data streaming, agentic frameworks, Data APIs, vector stores, and RAG architectures, self-serve analytics and AI.
Lead Execution of a complex and large Data and Analytics portfolio.
Data Modernization: Develop and implement a strategic roadmap to modernize legacy data and analytics ecosystems using Cloud and AI. Solve for data complexity by enabling data domains and data products for all consumption architypes and stakeholders including reporting, data science, AI/ML and analytics.
Architecture and Solution: Ensure data architecture and solutions align with enterprise-wide standards for Data, AI and Analytics.
Effectively communicate strategy, execution progress, and outcomes to diverse stakeholders and promote data capabilities through thought leadership and presentations.
AI Data Engineering leader responsible for Implementing AI data pipelines that integrate structured, semi-structured, and unstructured data to support AI and Agentic solutions.
Drive best practices in AI data engineering by establishing standardized processes, promoting cutting-edge technologies, and ensuring data quality and compliance across the enterprise.
Data and Analytics Management: Oversee the design, development, and maintenance of data pipelines, data warehouses, data lakes and reporting systems.
Leadership: Build, mentor, and lead a high-performing team including business data analysts, data engineers and release train engineers.
Drive efficiency and Productivity: Identify and champion developer productivity improvements across the end-to-end data management lifecycle. This includes researching and implementing innovative solutions such as AI-driven auto-generation of data pipelines, advanced DevOps practices for data and automated data quality frameworks.
Technology Evaluation & Adoption: Stay current with emerging trends in data engineering and AI/ML, design prototypes and conduct experiments, and recommend innovative tools and technologies to enhance data capabilities enabling business strategy.
Data Governance, Stewardship and Quality: Define and implement robust data management frameworks to ensure successful adoption of Enterprise Data Governance and Data Quality practices.
Budget Management: Effectively manage the budget and financials for the portfolio.
Develop deep partnerships and alignment with the portfolio and agile value stream frameworks. Experience with Agile at Scale and iterative development through cross-functional teams.
Partners with Technology, Data, AI Platform, ML Ops and Architecture teams to influence technology, data, platform and tooling strategy.