Senior Data Engineer - Health Data Pipelines & Analytics

CVS Health Corporation; Wisconsin; Hybrid; USD 83,000 - 222,000; Full time

Benefits offered by this job

Medical, dental, vision coverage

Paid time off

Retirement savings options

Job summary

CVS Health Corporation in Wisconsin is seeking a Sr. Data Engineer to design and implement data pipelines powering analytical capabilities and machine learning workloads.

This hands-on role emphasizes data engineering best practices and translating business needs into scalable technical solutions. You will help model the Core Data Domain, build robust ETL/ELT pipelines across multiple data sources, and collaborate with data scientists and analysts to enable BI and ML initiatives.

Qualifications

• Strong proficiency in SQL and ability to develop complex queries.
• Experience in Python with ETL pipelines.
• Experience deploying data pipelines in cloud environments (Azure, AWS, GCP).
• Knowledge of data governance best practices in a cloud environment.
• Excellent communication and collaboration with data scientists and analysts.

Responsibilities

• Model and architect data for analytics, reporting, and ML.
• Design and build ETL/ELT pipelines from multiple sources.
• Tune performance, partitioning, and clustering to reduce costs.
• Establish data quality, governance, and security policies.
• Collaborate with data scientists to integrate with BI and ML models.
• Create thorough documentation and mentor junior engineers.
• Design data infrastructure for analytical workloads and data marts.

Skills

SQL

Python

ETL/ELT

Cloud deployment

Data governance

Communication

Education

Bachelor's degree

Job description

CVS Health Corporation in Wisconsin is seeking a Sr. Data Engineer to design and implement data pipelines powering analytical capabilities and machine learning workloads.

This hands-on role emphasizes data engineering best practices and translating business needs into scalable technical solutions. You will help model the Core Data Domain, build robust ETL/ELT pipelines across multiple data sources, and collaborate with data scientists and analysts to enable BI and ML initiatives.