3 days ago
Remote, United StatesSenior
Responsibilities
- Design, build, and maintain ETL/ELT pipelines ingesting data from on-premise systems, AWS, Azure, and Snowflake.
- Develop feature stores and analytically optimized datasets that are clean, versioned, reproducible, and statistically valid for machine learning workflows.
- Build generative AI data pipelines for extracting, transforming, chunking, and loading structured and unstructured data into vector databases.
- Implement Snowflake data modeling, Snowpipe automation, and compute and storage optimization for analytics and AI workloads.
- Extract data from legacy systems using non-invasive approaches that preserve system stability.
- Design and manage cross-platform workflows using Airflow, AWS Step Functions, and Azure Data Factory.
- Resolve connectivity and access challenges involving PrivateLink, IAM, network segmentation, and firewall controls.
- Implement automated data quality, validation, and observability frameworks for drift, anomaly, and integrity detection.
- Optimize storage, compute usage, and query performance across Snowflake, AWS, and Azure.
- Partner with MLOps, Data Science, and AI teams to translate experimental use cases into scalable production data solutions.
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.
- Expert-level Snowflake architecture experience, including 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 interfacing with on-premise SQL systems, mainframe extracts, and flat files for modern cloud consumption.
- Understanding of machine learning data requirements, feature engineering, generative AI, vectorization, and embedding pipelines.
- Ability to work collaboratively, troubleshoot complex issues, manage multiple priorities, and deliver within enterprise environments.
Benefits
- Remote role open to candidates in any U.S. state.
- Occasional travel may be required.
- Sedgwick promotes work-life balance, a caring culture, and a diverse, equitable, and inclusive workplace.
- Reasonable accommodations will be considered when applicable and appropriate.
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.