Manager, Customer Insights Data Architecture

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 Lead the design and delivery of customer intelligence and enterprise data products that give the business trusted, AI-ready insight into customer behavior, needs, and experience. Combine advanced analytics, analytics engineering, and enterprise data architecture on Google Cloud Platform (GCP) to turn customer data into governed, scalable assets that support enterprise customer experience (CX) initiatives. What you will do: Customer Intelligence and Data Products • Define the customer intelligence strategy and roadmap, prioritizing enterprise data products (curated datasets, certified metrics, customer segments, and features) that serve business, analytics, and AI use cases. • Build and maintain scalable analytical solutions in BigQuery and GCP, using advanced SQL and analytics engineering practices (modular transformations, version control, automated testing, and CI/CD). • Develop customer segmentation and behavioral models in partnership with business leaders, translating insights on demographics, preferences, needs, and behaviors into actionable decisions. • Integrate survey, feedback, interview, and focus group data with behavioral and transactional data to create a unified view of the customer, using primary research selectively to fill gaps that enterprise data cannot. Enterprise Data Architecture and AI Readiness • Design and maintain enterprise customer data models (conceptual, logical, and physical) that are consistent, reusable, and optimized for analytical performance and cost in BigQuery. • Establish metadata management practices, including data catalogs, business glossaries, and documentation, so that data assets are discoverable and well understood. • Build and govern semantic and context layers that define customer entities, relationships, and business logic once and apply them consistently across BI, self-service analytics, and AI/ML applications. • Prepare AI-ready data assets by ensuring that data is well structured, documented, high quality, and accessible to machine learning models and generative AI use cases. Data Governance, Quality, and Compliance • Define and maintain trusted, certified customer metrics with clear ownership, definitions, and calculation logic, and retire conflicting or duplicate metrics. • Implement data quality standards, automated validation checks, monitoring, and alerting, and drives root-cause resolution of data issues with source system owners. • Document and maintain end-to-end data lineage from source systems to reports and models to support impact analysis and auditability. • Partner with data governance, privacy, legal, and security teams to ensure that customer data use complies with applicable regulations and internal policies (for example, consent, retention, and access controls). Collaboration and Enablement • Partner with business, analytics, engineering, and technology teams to define requirements, align priorities, and deliver data solutions that support enterprise CX initiatives. • Translate complex analyses into clear visualizations, reports, and presentations that guide decisions by executives and other stakeholders. • Lead cross-functional initiatives that improve customer satisfaction and experience outcomes using customer feedback and analytics. • Coach analysts and department teams on SQL, data