Sedgwick is seeking a Data Engineer to join its Transformation Office as a hands-on architect of the enterprise data supply chain for advanced analytics, data science, and AI initiatives. The role focuses on delivering high-fidelity, production-grade data that powers machine learning models, feature stores, and generative AI applications. You will design and implement data pipelines that bridge legacy on-premise systems (mainframes, SQL Server, DB2) with modern cloud platforms including Snowflake and AWS/Azure AI ecosystems. Responsibilities include designing, building, and maintaining resilient ETL/ELT pipelines that ingest data from on-premises systems, AWS services (S3, RDS), and Azure platforms (Blob Storage, Azure SQL), centralizing and curating data for Snowflake and downstream AI services. You will develop feature stores and analytically optimized datasets to support ML workflows, ensuring data is clean, versioned, reproducible, and statistically valid for Data Science teams. You will engineer pipelines enabling generative AI use cases, including extraction, transformation, chunking, and loading of structured and unstructured data into vector databases across AWS and Azure environments. You will act as a Snowflake power user, implementing advanced data modeling patterns, Snowpipe automation, and compute and storage optimization to support high-concurrency analytics and AI workloads. You will execute non-invasive data extraction from decades-old legacy systems while preserving stability and avoiding disruption to core business operations. You will design and manage co

Sedgwick is seeking a Data Engineer to join its Transformation Office as a hands-on architect of the enterprise data supply chain for advanced analytics, data science, and AI initiatives. The role focuses on delivering high-fidelity, production-grade data that powers machine learning models, feature stores, and generative AI applications. You will design and implement data pipelines that bridge legacy on-premise systems (mainframes, SQL Server, DB2) with modern cloud platforms including Snowflake and AWS/Azure AI ecosystems. Responsibilities include designing, building, and maintaining resilient ETL/ELT pipelines that ingest data from on-premises systems, AWS services (S3, RDS), and Azure platforms (Blob Storage, Azure SQL), centralizing and curating data for Snowflake and downstream AI services. You will develop feature stores and analytically optimized datasets to support ML workflows, ensuring data is clean, versioned, reproducible, and statistically valid for Data Science teams. You will engineer pipelines enabling generative AI use cases, including extraction, transformation, chunking, and loading of structured and unstructured data into vector databases across AWS and Azure environments. You will act as a Snowflake power user, implementing advanced data modeling patterns, Snowpipe automation, and compute and storage optimization to support high-concurrency analytics and AI workloads. You will execute non-invasive data extraction from decades-old legacy systems while preserving stability and avoiding disruption to core business operations. You will design and manage co