Senior Data Engineer

If you’re eager to make a real impact in the health care industry through your own meaningful contributions, explore a role in technology with CVS Health. Our journey calls for technical innovators and data visionaries: come help us pave the way. 

At CVS Health, we possess an extensive repository of healthcare data that spans over 150 million individuals, providing an unparalleled foundation for ambitious Data Engineers. In this role, you will engage with complex business challenges, harnessing modern tools and technologies to securely store, process, transform, and enrich terabyte to petabyte scale healthcare data. Your work will underpin data-driven business decisions and contribute to our mission of delivering industry-best data products / software with a customer-first mindset and team-oriented approach. 

As a Senior Data Engineer, you will be instrumental in designing, developing, and maintaining optimal data pipelines to assemble large and intricate datasets, catering to the business requirements of various CVS lines of business. Collaborating closely with teams, you will craft tools to provide actionable insights and integrate them with consumer touchpoints. 

In this role, you will:

• Architect and develop robust, scalable ETL/ELT pipelines using Cloud Dataflow, Cloud composer (Airflow), and Pub/Sub for both batch and streaming use cases. Leverage BigQuery as the central data warehouse and design integrations with other GCP services (e.g., Cloud storage, Cloud functions). 

• Build and optimize analytical data models in BigQuery. Implement partitioning, clustering, and materialized views for performance and cost efficiency. Ensure compliance with data governance, access controls, and IAM best practices.  

• Develop integrations with external systems (APIs, flat files etc.) using GCP-native or hybrid approaches. Utilize tools like Dataflow or custom Python/Java services on Cloud Functions or Cloud Run to handle transformations and ingestion logic. 

• Build automated CI/CD pipeline using Cloud Build, GitHub Actions, or Jenkins for deploying data pipeline code and workflows. Set up observability using Cloud Monitoring, Cloud Logging, and Error Reporting to ensure pipeline reliability. 

• Lead architectural decisions for data platforms and mentor junior engineers on cloud-native data engineering patterns. Promote best practices for code quality, version control, cost optimization, and data security in a GCP environment. Drive initiatives around data democratization, including building reusable datasets and data catalogs via Dataplex or Data Catalog. 

• Design, develop, and maintain enterprise AI/ML solutions and platforms that address complex business and customer challenges. Integrate Large Language Models (LLMs), generative AI capabilities, and AI agents into products and data ecosystems. Define the technical architecture and infrastructure required for AI applications, and build scalable platforms supporting model training, deployment, monitoring, governance, and lifecycle management while ensuring security, reliability, and operational excellence.

As leaders in healthcare, our analytics and engineering teams deliver innovative solutions to business problems by collaborating with cross-functional teams in a dynamic and agile environment. You will be part of a team that values collaboration and encourages innovative thinking at all levels. You will be intellectually challenged to solve problems associated with large scale complex, structured and unstructured data, that will allow you to grow your technical skills and engineering expertise.