Manager, Customer Insights Data Architecture

CVS Health•Woonsocket, RI

About The Position

Lead the design and delivery of customer intelligence and enterprise data products that provide 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 supporting enterprise customer experience (CX) initiatives. Define the customer intelligence strategy and roadmap, prioritizing enterprise data products such as curated datasets, certified metrics, customer segments, and features for business, analytics, and AI use cases. Build scalable analytical solutions in BigQuery and GCP, develop customer segmentation and behavioral models, and integrate research and feedback data with behavioral and transactional data to create a unified customer view. Design and maintain enterprise customer data models, metadata practices, and semantic and context layers. Prepare well-structured, documented, high-quality data assets for machine learning and generative AI. Establish trusted customer metrics, data quality controls, monitoring, lineage, and compliant data practices in partnership with governance, privacy, legal, and security teams. Collaborate with business, analytics, engineering, and technology teams to deliver solutions for enterprise CX initiatives. Communicate findings through visualizations, reports, and presentations; lead cross-functional initiatives to improve customer satisfaction and experience; and coach analysts and teams on SQL, data modeling, governance, and research methodology.

Requirements

5–7 years of experience in analytics, analytics engineering, data architecture, or data engineering, including customer or CX data.
Advanced SQL proficiency, including performance tuning, complex transformations, and query optimization.
Demonstrated experience with data modeling, such as dimensional modeling, and metadata management.
Working knowledge of data governance, data quality, data lineage, and customer data compliance requirements.
Experience delivering insights through data visualization and BI tools and presenting to technical and non-technical audiences.
Ability to solve problems, make sound decisions, and deliver results across cross-functional teams.
Growth mindset, including developing oneself and coaching others.
Bachelor's degree in data science, computer science, information systems, statistics, business analytics, or a related field, or equivalent professional experience or specialized training.

Nice To Haves

Experience building semantic layers, context layers, or knowledge models that support BI and AI/ML applications.
Experience with analytics engineering tools such as dbt or Dataform, and with data catalog and lineage tools.
Hands-on expertise with BigQuery and GCP, including designing and optimizing scalable analytical solutions.
Familiarity with preparing data for machine learning and generative AI use cases.
Experience in CX, customer analytics, or market research analytics.

Responsibilities

Define the customer intelligence strategy and roadmap, prioritizing enterprise data products for business, analytics, and AI use cases.
Build and maintain scalable analytical solutions in BigQuery and GCP using advanced SQL and analytics engineering practices, including modular transformations, version control, automated testing, and CI/CD.
Develop customer segmentation and behavioral models with business leaders, translating customer 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 customer view, using primary research selectively to address data gaps.
Design and maintain conceptual, logical, and physical enterprise customer data models optimized for analytical performance and cost in BigQuery.
Establish metadata management practices, including data catalogs, business glossaries, and documentation.
Build and govern semantic and context layers that consistently define customer entities, relationships, and business logic across BI, self-service analytics, and AI/ML applications.
Prepare AI-ready data assets that are structured, documented, high quality, and accessible to machine learning models and generative AI use cases.
Define and maintain trusted, certified customer metrics with clear ownership, definitions, and calculation logic; retire conflicting or duplicate metrics.
Implement data quality standards, automated validation checks, monitoring, and alerting, and drive 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 customer data use complies with regulations and internal policies, including consent, retention, and access controls.
Partner with business, analytics, engineering, and technology teams to define requirements, align priorities, and deliver data solutions for enterprise CX initiatives.
Translate analyses into clear visualizations, reports, and presentations that guide 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 modeling, governance practices, and research methodology, and promote enterprise knowledge sharing on customer insights.