Manager, AI Data Engineering

The Hartford•Columbus, OH

20h•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. The Enterprise Data Services Department’s Employee Benefits team is looking for a skilled Data Engineering Manager to join us. This is an exciting opportunity to join us on our multi-year Cloud Modernization journey. You will have an opportunity to engage in enabling well architected cloud-based data solutions for data and analytics in support of BI, Actuarial, Data Science, Finance, Operations and other key Data Consumers. o succeed in this role, you should be a strong critical thinker, technical acumen and be able to derive the root causes of business problems. 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

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 or Master's degree in Computer Science, Artificial Intelligence, or a related field.
8+ years of data engineering experience including Data solutions, SQL and NoSQL, Snowflake, ETL/ELT tools, CICD, Bigdata, Cloud Technologies (AWS/Google/AZURE), Python/Spark, Datamesh, Datalake or Data Fabric.
Specialized data engineering experience focused on supporting Generative AI technologies.
Strong hands-on experience implementing production ready enterprise grade AI data solutions.
Experience with prompt engineering techniques for large language models.
Experience in implementing Retrieval-Augmented Generation (RAG) pipelines, integrating retrieval mechanisms with language models.
Experience of vector databases and graph databases, including implementation and optimization.
Experience in processing and leveraging unstructured data for AI applications.
Proficiency in implementing scalable AI driven data systems supporting agentic solution (AWS Lambda, S3, EC2, Langchain, Langgraph).
Strong programming skills in Python and familiarity with deep learning frameworks such as PyTorch or TensorFlow.
Experience with building AI pipelines that bring together structured, semi-structured and unstructured data. This includes pre-processing with extraction, chunking, embedding and grounding strategies, semantic modeling, and getting the data ready for Models and Agentic solutions.
Experience in vector databases, graph databases, NoSQL, Document DBs, including design, implementation, and optimization. (e.g., AWS open search, GCP Vertex AI, Neo4j, Spanner Graph, Neptune, Mongo, DynamoDB etc.).
Experience in implementing data governance practices, including Data Quality, Lineage, Data Catalogue capture, holistically, strategically, and dynamically on a large-scale data platform.
Experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
Strong written and verbal communication skills and ability to explain technical concepts to various stakeholders.

Nice To Haves

Experience in multi cloud hybrid AI solutions.
AI Certifications
Experience in P&C or Employee Benefits industry
Knowledge of natural language processing (NLP) and computer vision technologies.
Contributions to open-source AI projects or research publications in the field of Generative AI.

Responsibilities

Manage and mentor team of data engineers.
Data Modernization: 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.
Effectively communicate strategy, execution progress, and outcomes to diverse stakeholders.
AI Data Engineering lead 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 and data 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.
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.