Senior Data Engineering and AI Platforms

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
** At CVS Health, we are building a world of health around every individual by creating a more connected, convenient and compassionate health experience. The
** Senior Data Engineering and AI Platforms
** will lead teams that develop secure, resilient and reusable data capabilities supporting analytics, operational decision-making and intelligent experiences. This leader will translate business priorities into scalable engineering roadmaps, guide cloud and data architecture, and establish disciplined practices for data quality, observability, model enablement and production operations. The role partners across product, architecture, security, governance, data science and business teams to deliver trusted outcomes while prioritizing safety, quality and responsible use of AI.

** Job Responsibilities
** Engineering Strategy and Platform Delivery   
* Set the technical direction and multi-quarter roadmap for enterprise data products, ingestion services, transformation frameworks and AI-ready data foundations.
* Lead the design and implementation of batch, streaming and event-driven solutions across cloud data platforms, balancing performance, reliability, maintainability and cost.
* Guide modernization of legacy integrations and reporting ecosystems into modular, cloud-native services with well-defined interfaces and reusable engineering patterns.
* Establish architecture guardrails for data modeling, storage, partitioning, orchestration, metadata, lineage and consumption layers.
* Oversee delivery from discovery through production readiness, including estimation, dependency management, testing, release planning, support transition and measurable adoption.  AI/ML and Intelligent Data Products  
* Partner with data scientists, ML engineers and product leaders to operationalize predictive, natural-language and generative AI use cases using governed enterprise data.
* Enable production ML and LLM workloads through curated datasets, feature pipelines, retrieval patterns, evaluation data, model monitoring and repeatable deployment workflows.
* Apply responsible AI practices, including traceability, human oversight, privacy safeguards, security review, explainability and ongoing quality assessment.
* Evaluate emerging AI-assisted development and agentic engineering approaches, introducing them where they improve delivery while maintaining enterprise controls.
* Promote experimentation through prototypes and proofs of value, then define the engineering path required to scale successful concepts safely.  Data Quality, Security and Operational Excellence   
* Embed automated validation, reconciliation, anomaly detection and data-quality controls across critical pipelines and data products.
* Implement observability that provides timely insight into freshness, completeness, failures, service health and downstream impact.
* Ensure solutions follow privacy, security, access-control, retention, audit and regulatory expectations applicable to healthcare and enterprise data.
* Define service objectives, incident response practices, recovery procedures and continuous-improvement routines for production platforms.
* Drive Dev Sec Ops  and Data Ops practices through source control, infrastructure automation, CI/CD, automated testing and environment promotion standards.  People Leadership and Cross-Functional Partnership  
* Lead, coach and develop data engineers and technical contributors; create clear accountability, growth plans and opportunities for broader ownership.
* Build an inclusive, high-performing culture grounded in care, purposeful innovation, accountability, safety and quality.
* Partner with business and technology stakeholders to turn complex needs into prioritized capabilities, clear acceptance criteria and transparent trade-off decisions.
* Communicate architecture, delivery health, risks, dependencies and investment choices to senior leaders in concise business terms.
* Coordinate internal teams, vendors and strategic partners, ensuring deliverables meet engineering, security and…