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The Hartford is an insurance and financial services company serving individuals, families, and small to midsize businesses. Founded in 1810, it provides auto, home, and business insurance alongside employee benefits and other insurance solutions. The company is also known for its AARP-endorsed auto and home insurance programs, which provide participating members with access to exclusive benefits and discounts.
Director of Data and Analytics, AI Engineering
Lead AI-focused data engineering for The Hartford’s actuarial analytics solutions. Build governed Snowflake data products, AI pipelines, and analytics capabilities that support actuarial decision-making.
Description
- Direct a complex, large-scale Data and Analytics portfolio.
- Create and execute a Cloud and AI roadmap to modernize legacy data and analytics ecosystems.
- Enable data domains and products for reporting, data science, AI/ML, and analytics users.
- Ensure data architectures and solutions comply with enterprise Data, AI, and Analytics standards.
- Present strategy, delivery progress, and outcomes to varied stakeholders through thought leadership and presentations.
- Develop AI data pipelines that combine structured, semi-structured, and unstructured data for AI and agentic solutions.
- Standardize AI data engineering practices while maintaining data quality and compliance.
- Oversee the design, development, and upkeep of data pipelines, warehouses, lakes, and reporting platforms.
- Recruit, mentor, and lead a high-performing team of business data analysts, data engineers, and release train engineers.
- Advance developer productivity throughout the data management lifecycle.
- Investigate and deploy AI-driven pipeline generation, DevOps practices, and automated data quality frameworks.
- Assess emerging data engineering and AI/ML technologies, build prototypes, run experiments, and recommend tools.
- Establish data management frameworks supporting enterprise data governance and data quality.
- Oversee portfolio budgets and financial management.
- Collaborate with Technology, Data, AI Platform, ML Ops, and Architecture teams to shape strategy and tooling.
- Convert actuarial requirements into scalable Snowflake data products and AI-enabled analytics solutions.
Requirements
- Bring 8–10 years of experience in data engineering, analytics solution delivery, actuarial analytics, or another data-intensive discipline.
- Have at least 3 years of experience supporting actuarial, insurance, pricing, reserving, modeling, underwriting, portfolio management, or financial analytics use cases.
- Demonstrate mastery of data engineering and architecture.
- Bring deep knowledge of data architecture patterns, warehouses, integration, lakes, domains, products, business intelligence, cloud capabilities, and data governance.
- Demonstrate technical expertise in LLMs, AI platforms, prompt engineering, LLM optimization, RAG architectures, and vector databases.
- Have experience enabling AI analytics, including analytic agents, natural-language analytics, governed knowledge bases, forecasting, monitoring, deep-dive analysis, and actuarial decision support.
- Be able to design, implement, and oversee Snowflake data pipelines, transformations, validations, and analytics consumption patterns.
- Show a track record of creating modern actuarial data products, governed consumption layers, semantic layers, reusable metrics, data quality controls, and analytics-ready models.
- Be able to apply Snowflake-native and cloud-integrated capabilities to actuarial workflows, predictive model outputs, monitoring, dashboards, and AI-enabled analytics.
- Be able to advance exploratory actuarial solutions into sustainable production assets with defined ownership, monitoring, documentation, and support expectations.
- Preferably understand actuarial measures and concepts including loss ratio, frequency, severity, rate adequacy, modeled indications, emergence monitoring, renewal health, and book assessment.
- Collaborate effectively with actuaries, data scientists, analytics and engineering teams, and business stakeholders.
- Have experience with Agile at Scale and iterative delivery across cross-functional teams.
Benefits
- Eligibility for short-term or annual bonuses.
- Long-term incentive opportunities.
- On-the-spot recognition programs.
- Hybrid work scheduling or a remote arrangement.
- Perks and benefits.
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