Bring your heart to CVS Health. Every one of us at CVS Health shares a single, clear purpose: Bringing our heart to every moment of your health. This purpose guides our commitment to deliver enhanced human-centric health care for a rapidly changing world. Anchored in our brand - with heart at its center - our purpose sends a personal message that how we deliver our services is just as important as what we deliver.

Our Heart At Work Behaviors™ support this purpose. We want everyone who works at CVS Health to feel empowered by the role they play in transforming our culture and accelerating our ability to innovate and deliver solutions to make health care more personal, convenient and affordable.

Position Summary
CVS Health invites a seasoned and highly skilled Principal Observability Data Engineer to lead the development and management of a data architecture and related data model to support our observability data lake and associated data pipelines. This position will design and maintain the data model, drive platform and pipeline optimization for scalability and performance, and devise solutions for comprehensive insight across platforms. The solutions developed and maintained by this role will enable proactive issue identification, streamlined operational triage, effective capacity planning, and seamless access for AI/ML platforms that require the data. The ideal candidate for this role has experience establishing and enforcing processes and standards for data and platform/solutions governance, Experience with data integration, optimization, and performance tuning, Hands-on experience implementing data architectures and pipelines in cloud environments, Experience with big data technologies, Proficiency in data modeling, data architecture design, and data pipeline development, and Experience with AI/ML integration and data requirements for AI/ML platforms. This role will collaborate closely with our Observability Engineering, Operations and Governance leaders, Application owners, and Business stakeholders to identify requirements and provide solutions and insights, and with our AI/ML teams to develop cutting-edge IT Operations AI/ML use cases and solutions.

• Lead the development and management of the data architecture and related data model for the observability data lake.
• Design and maintain a scalable, efficient, and effective data model to support critical use cases.

• Drive platform and pipeline optimization to ensure scalability and performance.
• Develop and implement solutions to provide comprehensive insights across platforms.

• Enable proactive issue identification through the development of advanced observability solutions.
• Support streamlined operational triage and effective capacity planning.

• Ensure seamless access for AI/ML platforms that require observability data.
• Collaborate with AI/ML teams to develop cutting-edge IT Operations AI/ML use cases and solutions.

• Work closely with Observability Engineering, Operations and Governance leaders, Application owners, and Business stakeholders to identify requirements and provide solutions and insights.
• Collaborate with AI/ML teams to understand their data needs, ensure the observability data architecture supports their requirements, and drive optimal use of the data within ML workloads.

• Develop and maintain comprehensive documentation for data architecture, data models, and data pipelines.
• Establish and enforce best practices for data management, governance, and security.

• Communicate effectively with stakeholders to provide updates on progress, challenges, and solutions.
• Ensure transparency and alignment with business objectives.

• 10+ years experience developing data architectures, data pipelines, and advanced data models at massive scale in complex ecosystems.
• 6+ years experience designing, implementing, and managing data architectures for large-scale operational data lakes, including observability and/or cybersecurity data and use cases.
• 6+ years crafting and maintaining high-volume data pipelines, with hands-on proficiency in industry-leading tools.
• Extensive experience with data integration, optimization, and performance tuning.
• Hands-on experience implementing data architectures and pipelines in cloud environments (e.g., AWS, Azure, GCP).
• Experience with big data technologies such as Hadoop, Spark, Kafka, and NoSQL databases.
• Proficiency in data warehousing solutions and ETL processes.
• Experience with observability tools and platforms (e.g., Splunk, Prometheus, Grafana).
• Strong scripting and programming skills (e.g., Python, Java, SQL).
• Strong understanding of observability and security data, IT and cyber operations, and monitoring tools.
• Proficiency in data modeling, data architecture design, and data pipeline development.
• Experience with AI/ML integration and data requirements for AI/ML platforms.
• Excellent problem-solving skills and the ability to troubleshoot complex technical issues.
• Strong communication and collaboration skills, with the ability to work effectively with cross-functional teams.
• Ability to interact with and influence individuals at all levels of the organization, and adept at understanding what type of information is most effective for a given audience (e.g., executives, developers, architects, etc.)
• Familiarity with DevOps practices and CI/CD pipelines.
• Knowledge of data governance and security best practices.

• Relevant certifications in data architecture, data modeling, or related fields are a plus (e.g., AWS Certified Solutions Architect, Google Professional Data Engineer, Microsoft Certified: Azure Data Engineer Associate).
• A Bachelor's or Master's degree in Computer Science, Information Systems, or an equivalent technical discipline, demonstrating a strong foundation in data architecture and systems.