Director Data and Analytics AI Engineering

Dir Data Engineering - GE06AE 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. The Hartford is seeking a Director of AI Data Engineering. 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.

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.

Qualifications:

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. 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. 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.

Work Arrangement:

This role can have a Hybrid work schedule out of the Hartford office 3 days a week (Tuesday through Thursday). Consideration may be given for placement at our Charlotte or Chicago hubs as well as a remote arrangement.

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: $156,000 - $234,000

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

About Us | Our Culture | What It’s Like to Work Here | Perks & Benefits Every day, a day to do right. Showing up for people isn’t just what we do. It’s who we are – and have been for more than 200 years. We’re devoted to finding innovative ways to serve our customers, communities and employees—continually asking ourselves what more we can do. Is our policy language as simple and inclusive as it can be? Can we better help businesses navigate our ever-changing world? What else can we do to destigmatize mental health in the workplace? Can we make our communities more equitable? That we can rise to the challenge of these questions is due in no small part to our company values that our employees have shaped and defined. And while how we contribute looks different for each of us, it’s these values that drive all of us to do more and to do better every day.

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