Senior Data Engineer — ETL & Analytics Pipelines

CVS Health; Utah; Presencial; USD 83.000 - 222.000; Jornada completa

Descripción de la vacante

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

This hands-on role requires translating business requirements into scalable technical solutions and modeling the Core Data Domain to support analytics, BI, and ML workloads. You will work with data scientists and analysts to design data solutions, ensure data quality, and document pipelines while contributing to a cost‑effective, secure data infrastructure.

Formación

• 5+ years of data engineering experience.
• Strong SQL proficiency and complex query development.
• Python experience with ETL pipelines.
• Experience deploying data pipelines in cloud environments (Azure, AWS, GCP).
• Knowledge of data governance in cloud environments.
• Strong communication and collaboration with data scientists and product owners.

Responsabilidades

• Model and architect data infrastructure for analytics and ML workloads.
• Design and build ETL/ELT pipelines from multiple sources.
• Optimize performance, partitioning, and cost of data queries.
• Establish data quality standards and governance frameworks.
• Collaborate with data scientists and analysts on data solutions.
• Document pipelines and mentor junior data engineers.
• Understand data warehousing concepts and build data marts.

Conocimientos

SQL

Python

ETL pipelines

Cloud environments

Data modeling

Data governance

Communication skills

Educación

Bachelor's degree

Descripción del empleo

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

This hands-on role requires translating business requirements into scalable technical solutions and modeling the Core Data Domain to support analytics, BI, and ML workloads. You will work with data scientists and analysts to design data solutions, ensure data quality, and document pipelines while contributing to a cost‑effective, secure data infrastructure.