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# Senior Data Engineer

- **Company:** CVS Health
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $83,430.0 - $222,480.0
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Microsoft Azure, Big Data, Cloud Computing, Data Governance, Data Infrastructure, 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, Core Data, Data Pipelines
- **Published:** October 2, 2026
- **Apply:** https://dejobs.org/x/x/48AFE9E84F974ACD9DA207131700C82F/job/

## About the Role

* Strong proficiency in SQL and experience in developing complex queries
 * Experience in Python, specifically with ETL pipelines
 * Strong proficiency in SQL and experience in developing complex queries
 * Experience deploying data pipelines in a cloud environment (Azure, AWS, GCP)
 * Knowledge of data governance best practices in a cloud environment.
 * Excellent communication and interpersonal skills, with the ability to collaborate effectively with data scientists, analysts, and product owners.

Preferred Qualifications

 * Experience with machine learning flows on GCP
 * Experience working with healthcare data (Tuva or OMOP Models a plus), * Bachelor's degree preferred/specialized training/relevant professional qualification.

## Description

We're seeking a Sr. Data Engineer to design and implement data pipelines powering 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 also have a key role in modeling the Core Data Domain to best support various access patterns and use-cases., * Data Modeling & Architecture: Develop and optimize data models and schemas to support analytics, reporting, and machine learning requirements.

 * 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 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.
 * Design and architect data infrastructure analytical workloadsUnderstanding of data warehousing concepts, dimensional modeling, and building data marts.