Data Engineer

By joining Sedgwick , you'll be part of something truly meaningful. It’s what our 33,000 colleagues do every day for people around the world who are facing the unexpected. We invite you to grow your career with us, experience our caring culture, and enjoy work-life balance. Here, there’s no limit to what you can achieve. Newsweek Recognizes Sedgwick as America’s Greatest Workplaces National Top Companies Certified as a Great Place to Work® Fortune Best Workplaces in Financial Services & Insurance Data Engineer PRIMARY PURPOSE : The Gen AI Engineer within the Transformation Office serves as the hands‑on architect of the enterprise data supply chain for the organization’s most advanced analytics, data science, and AI initiatives. This role performs the critical engineering work required to deliver high‑fidelity, production‑grade data that powers machine learning models, feature stores, and generative AI applications. Operating as a “day‑one” builder, the Gen AI Engineer designs and delivers data pipelines that bridge legacy on‑premise systems—including mainframes, SQL Server, and DB2—with modern cloud platforms such as Snowflake and AWS/Azure AI ecosystems. The role ensures that data is not merely transferred, but deliberately engineered to meet the statistical, performance, and governance requirements of model training, inference, and RAG‑based AI systems. ESSENTIAL FUNCTIONS AND RESPONSIBILITIES Designs, builds, and maintains resilient ETL/ELT pipelines that ingest data from on‑premise systems, AWS services (S3, RDS), and Azure platforms (Blob Storage, Azure SQL), centralizing and curating data for consumption in Snowflake and downstream AI services. Develops and maintains feature stores and analytically optimized datasets that support machine learning workflows, ensuring data is clean, versioned, reproducible, and statistically valid for Data Science teams. Engineers data pipelines that enable generative AI use cases, including the automated extraction, transformation, chunking, and loading of structured and unstructured data into vector databases across AWS and Azure environments. Acts as a Snowflake power user and technical lead, implementing advanced data modeling patterns, Snowpipe automation, and compute and storage optimization to support high‑concurrency analytics and AI workloads. Executes non‑invasive data extraction strategies to unlock mission‑critical data from decades‑old legacy systems while preserving system stability and avoiding...

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