Senior Data Engineer: Cloud Data Pipelines & Analytics

CVS Health Corporation; Colorado; Hybrid; 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 Corporation is seeking a Sr. Data Engineer to design and implement data pipelines powering analytical capabilities. This hands-on role requires translating business requirements into technical solutions and supporting a dedicated team for analytics and data science workloads.

You will model the Core Data Domain, design scalable data infrastructure, and collaborate with data scientists and analysts to deliver robust data solutions for BI and ML initiatives.

Qualifications

• Strong SQL skills and ability to write complex queries.
• Experience with Python for ETL pipelines.
• Experience deploying data pipelines in cloud environments.
• Knowledge of data governance best practices in cloud settings.
• Excellent communication and collaboration with data scientists and analysts.

Responsibilities

• Model and optimize data models and schemas for analytics and ML.
• Design and build ETL/ELT pipelines from multiple sources.
• Tune performance with partitioning and clustering to reduce costs.
• Establish data quality standards and governance policies.
• Collaborate with data scientists to design data solutions for BI and ML.
• Create documentation and share best practices; mentor junior engineers.
• Design data infrastructure and data marts in support of analytical workloads.

Skills

SQL

Python

ETL pipelines

Data governance

Communication

Education

Bachelor's degree

Tools

Azure

AWS

GCP

Job description

CVS Health Corporation is seeking a Sr. Data Engineer to design and implement data pipelines powering analytical capabilities. This hands-on role requires translating business requirements into technical solutions and supporting a dedicated team for analytics and data science workloads.

You will model the Core Data Domain, design scalable data infrastructure, and collaborate with data scientists and analysts to deliver robust data solutions for BI and ML initiatives.