Senior Data Engineer — Health Analytics & Data Pipelines

CVS Health; Clayton (NC); On-site; USD 83,000 - 222,000; Full time

Benefits offered by this job

Medical, dental, and vision coverage

Paid time off

Retirement savings options

Wellness programs

Job summary

CVS Health seeks a Sr. Data Engineer to design and implement data pipelines powering analytical capabilities.

This hands-on role requires experience with data engineering best practices and translating business requirements into technical solutions. You will be part of a team creating datasets for analytic and data science workloads, modeling the Core Data Domain to support various access patterns and use-cases.

Qualifications

• 5+ years of data engineering experience.
• Strong SQL skills and ability to write complex queries.
• Proficient in Python for ETL pipelines.
• Experience deploying data pipelines in cloud environments (Azure, AWS, GCP).
• Knowledge of data governance practices in cloud contexts.
• Excellent communication and collaboration with data scientists and analysts.

Responsibilities

• Model and architect data solutions to support analytics and ML workloads.
• Design and build ETL/ELT pipelines ingesting data from multiple sources.
• Tune performance, partitioning, and clustering to optimize queries and costs.
• Establish data quality and governance standards for storage and access.
• Collaborate with data scientists to integrate with BI and ML models.
• Create documentation and share best practices; mentor junior engineers.
• Design analytical data infrastructure and data marts with dimensional modeling.

Skills

SQL

Python

ETL pipelines

Cloud technologies

Data governance

Communication

Education

Bachelor's degree

Job description

CVS Health seeks a Sr. Data Engineer to design and implement data pipelines powering analytical capabilities.

This hands-on role requires experience with data engineering best practices and translating business requirements into technical solutions. You will be part of a team creating datasets for analytic and data science workloads, modeling the Core Data Domain to support various access patterns and use-cases.