Senior Data Engineer

CVS Health

1d•Onsite

About The Position

We’re building a world of health around every individual — shaping a more connected, convenient and compassionate health experience. At CVS Health®, you’ll be surrounded by passionate colleagues who care deeply, innovate with purpose, hold ourselves accountable and prioritize safety and quality in everything we do. Join us and be part of something bigger – helping to simplify health care one person, one family and one community at a time. Position Summary We're seeking a Sr. Data Engineer to design and implement data pipelines that power analytical capabilities. This hands-on role requires an understanding of data engineering best practices and the ability to translate business requirements into technical solutions. You will be part of a dedicated team creating datasets for financial and analytic workloads.

Requirements

5+ years of applicable work experience
Proficiency in Python, specifically with ETL pipelines.
Strong proficiency in SQL and experience in developing complex queries.
Familiarity with pySpark, DBT, or other similar frameworks.
Experience deploying data pipelines in a cloud environment (Azure, AWS, GCP).
Understanding of data warehousing concepts, dimensional modeling, and building data marts.
Excellent communication and interpersonal skills, with the ability to collaborate effectively with data scientists, analysts, and product owners.

Nice To Haves

Knowledge of data governance best practices in a cloud environment.
Experience with data design in BigQuery
Experience working with the Epic data model.
Experience working with healthcare data (Claims and Admissions)

Responsibilities

Design and build ETL/ELT data pipelines to ingest, process, and transform large datasets from multiple sources.
Implement best practices for performance tuning, partitioning, and clustering to optimize data queries and reduce costs.
Establish and enforce data quality standards, data governance frameworks, and security policies for data storage and access.
Develop and optimize data models and schemas to support analytics, reporting, and machine learning requirements.
Collaborate with data scientists and analysts to design data solutions that integrate with BI tools and machine learning models.
Create comprehensive documentation for data pipelines, workflows, and processes.
Share best practices and mentor junior data engineers.
Design and architect data infrastructure analytical workloads.