Data Engineer

CVS Health•Rathdrum, ID

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 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 analytic and data science workloads. You will work with other Data Engineers and Sr. Data Engineers on designing, implementing, and testing data pipelines.

Requirements

2+ years of applicable work experience
Proficiency in Python, specifically with ETL pipelines
Strong proficiency in SQL and experience in developing complex queries
Experience deploying data pipelines in a cloud environment (any of Azure, AWS, GCP)
Excellent communication and interpersonal skills, with the ability to collaborate effectively with data scientists and analysts.

Nice To Haves

Experience working with healthcare data, especially Epic.
Experience using GCP’s BigQuery
Knowledge of data governance best practices in a cloud environment.
Experience working with Machine Learning processes.

Responsibilities

Design and build ETL/ELT data pipelines to ingest, process, and transform datasets from multiple sources.
Implement best practices for performance tuning, partitioning, and clustering to optimize data queries.
Follow 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.