3 days ago
Remote, United StatesEntry Level
Responsibilities
- Design, build, and maintain ETL/ELT pipelines ingesting data from on-premise systems, AWS, and Azure for Snowflake and downstream AI services.
- Develop feature stores and analytically optimized datasets that are clean, versioned, reproducible, and suitable 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 high-concurrency analytics and AI workloads.
- Extract data non-invasively from legacy systems while preserving system stability and avoiding disruption to core operations.
- 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 with partner teams.
- Implement automated data quality, validation, and observability frameworks to detect drift, anomalies, and integrity issues.
- 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.
- 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 is preferred.
- Expert-level proficiency with 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.
- Strong understanding of machine learning data needs, feature engineering, generative AI, vectorization, and embedding pipelines.
- Ability to navigate enterprise bureaucracy and technical debt while delivering production code quickly.
- Ability to work in a team environment and meet or exceed performance competencies.
- Clear conceptual thinking, judgment, troubleshooting, problem-solving, analysis, discretion, and ability to manage multiple priorities and deadlines.
Benefits
- Sedgwick promotes work-life balance and a caring workplace culture.
- The company is recognized as a Great Place to Work and as a leading workplace in financial services and insurance.
- Reasonable workplace accommodations will be considered when applicable and appropriate.
- Travel is required as needed.
- Sedgwick is an Equal Opportunity Employer and Drug-Free Workplace.
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.