2 months ago
Remote, United StatesSenior
Responsibilities
- Design, build, and maintain ETL/ELT pipelines integrating on-premise systems, AWS, Azure, Snowflake, and downstream AI services.
- Develop feature stores and analytically optimized, clean, versioned, reproducible datasets 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, performance tuning, 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.
- Partner with IT, database, infrastructure, and security teams on connectivity, access, networking, and production approvals.
- Implement automated data quality, validation, observability, drift detection, anomaly detection, and integrity controls.
- Optimize storage, compute, and query performance across Snowflake, AWS, and Azure.
- Support MLOps, Data Science, and AI teams by translating experimental use cases into scalable production data solutions.
- Perform other duties as assigned and travel as required.
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 from an accredited college or university is preferred.
- Expert-level proficiency in Snowflake architecture, 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 working with on-premise SQL, 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, navigate enterprise bureaucracy and technical debt, troubleshoot problems, manage priorities, and meet deadlines.
Benefits
- Work-life balance and a caring workplace culture.
- Equal opportunity and drug-free workplace.
- Reasonable accommodations 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.