Senior Staff AI Data Engineer - Hybrid

The Hartford•Columbus, OH

1d•$135,040 - $202,560•Hybrid

About The Position

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. Join our team as a Senior Staff AI Data Engineer and lead the charge in developing cutting-edge AI solutions and data engineering strategies. Embrace our core values of innovation, collaboration, and excellence as you unlock unparalleled growth opportunities in the dynamic field of AI and data engineering. Shape the future of technology with us! Apply now to be part of our innovative journey and make a significant impact! 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).

Requirements

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
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

Nice To Haves

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

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