Director, AI Data 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.   

         

We are seeking a highly skilled Director,  AI Data Engineering, to join our Employee benefits data team. This role requires strategic thinking, expertise in data engineering practices, knowledge of AI technologies, The candidate should be versed in real-time data streaming, agentic frameworks, Data APIs, vector stores, and RAG architectures, and have a track record of enabling self-serve analytics and AI use cases. This role will be a hands on leadership role.

This role will have a Hybrid work schedule, with the expectation of working in an office (Columbus, OH, Chicago, IL, Hartford, CT or Charlotte, NC) 3 days a week (Tuesday through Thursday). Must be eligible to work in the US without company sponsorship. The company will not support the STEM OPT I-983 Training Plan endorsement for this position

Responsibilities:

• 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.
• Real-Time Data Streaming: Design, build and maintain scalable real-time data pipelines for efficient ingestion, processing, and delivery.
• 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.
• 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.
• 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:

• 12+ years in data engineering, data management and building large-scale data ecosystems.
• Bachelor's or Master’s degree in Computer Science, Data Science or a related field.
• 3+ years in senior leadership roles managing large and complex data and analytics portfolio with large size teams.
• Proven strategic and innovative thinker with a track record of enabling transformative data capabilities.
• Mastery level data engineering and architecture skills, including deep expertise in data architecture patterns, data warehouse, data integration, data lake