Manager, Cloud and Data Platform Engineer

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
The Manager, Cloud & Data Platform Engineer owns the Retail Resource Center’s application and data technology stack in the Google Cloud environment, working in a fast-paced setting where reliability, security, data quality, and speed of delivery are critical. Work efforts are focused on managing application deployment, cloud infrastructure, and data pipelines across Cloud Run, Kubernetes (GKE), Redis, BigQuery, and other Google Cloud services to support reliable releases, system stability, scalability, trusted analytics, and operational efficiency. The role also leverages AI to accelerate development and automation, including the development of AI agents.

Key Responsibilities

• Owns the application and data technology stack in Google Cloud, including architecture, availability, performance, and lifecycle management of Cloud Run services, Google Kubernetes Engine (GKE) clusters, Redis databases, BigQuery datasets, and supporting services such as Cloud Composer, Cloud Storage, Cloud SQL, Secret Manager, and Artifact Registry.

• Manages code deployment and release processes. Designs, builds, and maintains CI/CD pipelines (e.g., Cloud Build, GitHub Actions) for application and data workloads, including pipeline code, DAGs, and SQL transformations, and applies release strategies to keep deployments fast, repeatable, and low risk.

• Oversees containerization and deployment practices, including configuration management and container image lifecycle management.

• Designs, builds, and maintains batch and streaming data pipelines that ingest data into BigQuery.

• Develops and manages the analytics data layer in BigQuery, including dataset design, and SQL-based transformations that produce data models for reporting and analysis.

• Implements data quality checks, schema management, lineage, and pipeline observability. Defines and monitors data freshness and reliability targets, and troubleshoots failed or delayed data loads.

• Applies data governance and security controls, including dataset- and column-level access, handling of sensitive data, and retention policies in line with company privacy and compliance standards.

• Partners with analysts, product owners, and business stakeholders to translate reporting needs into reliable datasets and dashboards and supports self-service access to trusted data.

• Defines and manages infrastructure as code with version-controlled, peer-reviewed changes. Automates provisioning, environment setup, and routine operational tasks to improve efficiency and consistency across development, staging, and production.

• Implements monitoring, logging, tracing, and alerting for applications, infrastructure, and data pipelines. Defines and tracks service levels and builds dashboards that give stakeholders clear visibility into platform health and performance.

• Leads incident response, root cause analysis, and post-incident reviews for production issues. Participates in an on-call rotation and develops runbooks and operating procedures.

• Manages IAM roles, service accounts, network configuration, secrets, and vulnerability scanning in line with company security and compliance standards. Partners with security teams on audits and remediation.

• Plans for capacity, autoscaling, backup, and disaster recovery for applications and data stores to ensure the platform can scale with business needs.

• Collaborates with software engineering, security, and operations teams to understand requirements, answer cloud architecture and deployment questions, and develop solutions to technical challenges.

• Leverages AI to accelerate development and automation across application, infrastructure, and workloads, improving delivery speed and consistency.

• Develops AI agents to automate workflows and operational tasks, accelerating delivery and reducing manual effort.

• Evaluates new Google Cloud services, tools, and practices, and contributes technical input to roadmap and strategic planning sessions.

• Seeks opportunities to enhance their knowledge and skills through professional development, training, and certifications, and explores new tools and techniques to improve platform reliability and engineering efficiency.

Required Qualifications

• 3+ years of experience in DevOps, site reliability, cloud, platform, or data engineering, including hands-on production experience with Google Cloud Platform (Cloud Run, GKE, Redis, BigQuery) and building or supporting production data pipelines
• Working knowledge of Google Cloud services, including Cloud Run, GKE, Redis, BigQuery, and IAM

• Strong understanding of Kubernetes and Docker

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