This role will have a Hybrid work schedule, with the expectation of working in an office location (Hartford, CT; Chicago, IL; Columbus, OH; and Charlotte, NC) 3 days a week (Tuesday through Thursday).

Primary Job Responsibilities

• Lead the implementation of AI data pipelines integrating structured, semi-structured, and unstructured data to support AI and agentic solutions, including preprocessing techniques such as extraction, chunking, embedding, and grounding (e.g., RAG, retrieval frameworks)
• Develop AI-driven data systems that enhance data capabilities while ensuring adherence to industry best practices
• Implement and optimize Retrieval-Augmented Generation (RAG) architectures and integrate them with enterprise data platforms
• Design, build, and optimize scalable batch and streaming data pipelines with a focus on performance, resiliency, and operational efficiency
• Develop and maintain real-time data streaming pipelines using technologies such as Snowpipe
• Develop data domains and data products to support reporting, analytics, AI/ML, and data science use cases
• Ensure the reliability, availability, and scalability of data pipelines through monitoring, alerting, and incident management
• Implement reliability engineering best practices, including fault tolerance, redundancy, and disaster recovery
• Drive engineering discipline across data platforms, including observability, data quality, lineage, and governance
• Collaborate with DevOps and infrastructure teams to enable seamless deployment and operation of data systems
• Partner with cross-functional teams to integrate data and AI solutions into business processes and enterprise systems
• Provide architectural leadership in partnership with Data Architects, including defining technical standards and influencing enterprise-wide practices
• Develop and integrate graph database solutions to support complex data relationships within AI systems
• Apply GenAI approaches to insurance-specific data use cases and challenges
• Lead the development of AI-ready data foundations that support scalable, production-grade solutions
• Ensure data platforms remain resilient, governed, and cost-efficient, aligned with enterprise cloud and data strategies
• Mentor junior engineers and contribute to communities of practice, promoting best practices, reusable patterns, and engineering standards
• Stay current with advancements in GenAI and apply relevant technologies and methodologies to platform evolution

Skills

• Strong technical expertise in AI-driven data solutions leveraging modern cloud platforms
• Deep expertise in core data engineering, including advanced SQL, data modeling, and query performance tuning
• Strong experience in ETL/ELT architecture, orchestration frameworks, and pipeline optimization
• Experience working across teams with strong communication and stakeholder management skills
• Proven ability to mentor and develop AI and data engineering talent
• Knowledge of emerging AI and data engineering design patterns
• Strong planning, organization, and execution capabilities
• Ability to lead in a lean, agile, and fast-paced environment, leveraging Scaled Agile practices
• Strong analytical and problem-solving skills with the ability to translate business requirements into technical solutions
• Demonstrated leadership capability to own architecture decisions and drive cross-team alignment
• Effective collaboration, decision-making, and relationship-building skills
• Strong interpersonal skills with the ability to provide thought leadership in a dynamic environment

Qualifications

• Candidates must be authorized to work in the US without company sponsorship. The company will not support the STEM OPT I-983 Training Plan endorsement for this position.
• Bachelor’s degree in Computer Science, Artificial Intelligence, or a related field
• 8+ years of data engineering experience with deep expertise in SQL, data modeling, and large-scale data processing systems
• Proven experience designing and optimizing ETL/ELT pipelines and orchestration frameworks in enterprise environments
• Experience supporting Generative AI data engineering use cases
• Hands-on experience implementing production-ready, enterprise-grade GenAI data solutions
• Experience implementing RAG pipelines, including retrieval, chunking, embedding, and grounding techniques
• Experience operationalizing GenAI pipelines in production environments
• Hands-on experience with cloud ecosystems (AWS, GCP, Azure, Snowflake) and Python-based data engineering stacks
• Proven ability to deliver resilient, governed, and cost-efficient data platforms at scale
• Experience with vector databases and graph databases, including design and optimization
• Experience working with unstructured data for GenAI applications
• Experience implementing data governance practices, including data quality, lineage, and data cataloging at scale
• Proficiency in building AI data pipelines that integrate structured and unstructured data with preprocessing techniques
• Strong programming skills in Python
• Strong communication skills and ability to explain technical concepts to a broad set of stakeholders

Preferred Qualifications

• Experience designing multi-cloud or hybrid AI data solutions
• AI-related certifications
• Experience in the P&C insurance industry
• Contributions to open-source AI projects or research in Generative AI

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: