18 hours ago
Remote, United StatesSenior
Responsibilities
- Design, build, and maintain resilient ETL/ELT pipelines across on-premise systems, AWS, Azure, and Snowflake.
- Develop feature stores and analytically optimized, versioned, reproducible datasets for machine learning workflows.
- Build data pipelines for generative AI use cases, including extraction, transformation, chunking, vectorization, and loading into vector databases.
- Implement Snowflake data modeling, Snowpipe automation, and compute and storage optimization.
- Extract data non-invasively from legacy systems while preserving operational stability.
- Manage cross-platform workflows using Airflow, AWS Step Functions, and Azure Data Factory.
- Resolve connectivity, access, network, and security integration issues with IT, database, infrastructure, and security teams.
- Implement automated data quality, validation, anomaly detection, data drift, and observability frameworks.
- Optimize storage, compute usage, and query performance across Snowflake, AWS, and Azure.
- Partner with MLOps, Data Science, and AI teams to turn experimental use cases into scalable production data solutions.
- Travel as required and perform other duties as assigned.
Requirements
- Six years of hands-on data engineering experience building production-grade pipelines for Data Science and AI in multi-cloud environments, or an equivalent combination of education and experience.
- Master's degree in Computer Science, Data Engineering, or a related field is preferred.
- Expertise in Snowflake architecture, data sharing, performance tuning, and integration with external cloud AI services.
- Advanced hands-on knowledge of AWS data services including S3, Glue, and Lambda, and Azure data services including Data Factory and Synapse.
- Mastery of Python, SQL, and PySpark.
- Deep experience with data orchestration and containerization using Docker.
- Experience working with on-premise SQL, mainframe extracts, and flat files for modern cloud consumption.
- Understanding of machine learning data requirements, feature engineering, and generative AI vectorization and embedding pipelines.
- Ability to work collaboratively, troubleshoot complex issues, manage multiple priorities, meet deadlines, and deliver in an enterprise environment.
Benefits
- Sedgwick describes a caring culture, work-life balance, career growth opportunities, and an inclusive workplace.
- Reasonable accommodations will be considered when applicable and appropriate.
- Travel is required as needed.
Categories
Data Engineering
About Sedgwick
Sedgwick provides third‑party claims administration, risk and benefits services for employers, insurers, and public entities, spanning workers’ compensation, property and liability, disability and absence, and product recall. It sells outsourced claims management and related technology on a fee‑for‑service basis, supported by analytics and AI‑enabled tools, across operations in more than 80 countries. Founded in 1969 and headquartered in Memphis, Tennessee, Sedgwick is privately held under The Carlyle Group.