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# Senior Data Engineer

- **Company:** CVS Health
- **Location:** United States
- **Experience:** Expert
- **Salary:** $83,430.0 - $222,480.0
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Microsoft Azure, Big Data, BigQuery, Cloud Computing, Data Architecture, Data Governance, Data Integration, Extract Transform Load (ETL), Data Mart, Data Systems, Data Warehousing, Database Queries, Dimensional Modeling, Python (Programming Language), Machine Learning, Performance Tuning, DataOps, SQL Databases, Google Cloud, Data Storage Technologies, Build Management, Pyspark, Data Pipelines
- **Published:** May 30, 2026
- **Apply:** https://www.juju.com/job/00000000g3ctez

## About the Role

+ 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.

Preferred Qualifications

 + 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)

Education and Experience

 + College degree or certification in related fields

## Description

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

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

 + Data Quality & Governance: Establish and enforce 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. Share best practices and mentor junior data engineers.


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