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# Data Engineer

- **Company:** CVS Health
- **Location:** United States
- **Experience:** Experienced
- **Salary:** $64,890.0 - $173,040.0
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Microsoft Azure, BigQuery, Cloud Computing, Data Governance, Data Integration, Extract Transform Load (ETL), Data Systems, Python (Programming Language), Machine Learning, Performance Tuning, DataOps, SQL Databases, Google Cloud, Data Storage Technologies, Build Management, Data Pipelines
- **Published:** May 15, 2026
- **Apply:** https://www.juju.com/job/00000000fzlyrw

## About the Role

+ 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.

Preferred Qualifications

 + 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., College degree or certification in related fields

## Description

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. Your key responsibilities are:

 + Data Pipeline Development: Design and build ETL/ELT data pipelines to ingest, process, and transform datasets from multiple sources.

 + Performance Optimization: Implement best practices for performance tuning, partitioning, and clustering to optimize data queries.

 + Data Quality & Governance: follow data quality standards, data governance frameworks, and security policies for data storage and access.

 + Data Modeling & Architecture: Develop and optimize data models and schemas to support analytics, reporting, and machine learning requirements.

 + Data Integration & Transformation: Collaborate with data scientists and analysts to design data solutions that integrate with BI tools and machine learning models

 + Documentation & Knowledge Sharing: Create comprehensive documentation for data pipelines, workflows, and processes.


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