The Vice President of Data Strategy & Architecture will set RxBenefits’ enterprise data direction and define the target-state enterprise data architecture and operating model that enables trusted, scalable analytics, AI, and digital experiences. This leader will translate business priorities into a pragmatic roadmap for data platforms, integration patterns, and an enterprise semantic/metrics layer to ensure consistent meaning across reporting and decision-making. The role also builds and scales fit-for-purpose data governance ownership and stewardship, standards, data quality controls, metadata/lineage, and access/privacy policies. The VP partners with business and technology leaders to operationalize data products and reporting while meeting privacy, security, and regulatory requirements. 

Key Responsibilities:

1. Enterprise Data Strategy & Vision:
   • Define and advance enterprise long-term data vision and roadmap, aligned to business strategy, priorities, and growth objectives. 
   • Position data as a strategic asset to support decision-making via analytics, AI augmented decision support, AI models, and digital innovation. 
   • Lead data and analytics team to an AI first operational model, defining an AI centric SDLC for data & analytics operations.
   • With business stakeholder define KPIs and value realization metrics tied to value creation.
   • Identifying opportunities to leverage data, analytics, and AI for growth, risk reduction, margin improvements. 
   • Enable the business with centrally governed semantic model supporting data platform, analytics tools, data integration tools, enterprise AI, automation, and other needs. 
   • Remove technical and organizational fragmentation and silos in the company, ensuring a single data & analytics vision.
2. Data Architecture and Infrastructure: 
   • Mature the existing data architecture framework and capability, building a holistic architecture supporting data production through consumption.
   • Align Data Engineering and Analytics/Reporting to define and enforce enterprise semantic standards, shared business definitions, and governed metrics.
   • Ensure analytics, dashboards, and downstream data products consistently leverage a common enterprise semantic layer 
   • Managing the full data lifecycle – ensuring design and maintaining conceptual, logical and physical data models via strong architecture frameworks for master data management. 
   • Establish governance checkpoints within the data product and analytics lifecycle to prevent metric drift, semantic inconsistencies, and reconciliation issues.
   • Drive adoption of standardized definitions through data catalogs, reporting layers, and analytics tools.
   • Own authority of vendor and technology decisions for data platform & analytics tools. 
3. Data Governance & Program Sponsorship:
   • Lead the development and adoption of a data governance framework with clear roles and accountabilities, including standards and operating models.
   • Sponsor and champion the data governance program across the organization.
   • Establish and mature business data stewardship across domains, with clear ownership, accountability, and success measures.
   •  Lead and facilitate enterprise data governance councils and forums to drive alignment and resolve cross-domain issues.
   • Foster cross-functional collaboration to ensure data governance and data product operating model aligns with business priorities. 

1. Driving Business Value:
   • Ensure data roadmap and governance initiatives deliver measurable outcomes such as faster access to trusted data, reduced reporting rework, and improved decision confidence.
   • Align data governance priorities to high-value business use cases across pricing, finance, operations, and client reporting.
   • Define and monitor data quality standards and KPIs (accuracy, completeness, timeliness, consistency).
   • Implement processes and tooling for data profiling, data cataloging, and lineage to improve transparency, issue resolution, and change management. 
   • Ensure a “single source of truth” for critical enterprise data domains.

Qualifications:

• Proven experience of 12+ years in data architecture, analytics, data governance, data management, or related fields, with a minimum of 5+ years in a senior leadership role.
• Strong strategic planning and communication skills, with a demonstrated ability to influence at the executive level.
• Experience in leading complex, cross-functional teams and aligning data investments with business priorities.
• In-depth knowledge of data governance frameworks, tools, and best practices and experience driving business and technical stakeholder partnership for data governance success
• Ability to drive cultural change and foster a data-driven decision-making environment

Desired Outcomes (12-18 months):

• A clear, enterprise-wide data governance operating model with defined ownership and accountability.
• An analytics operating model supporting centrally owned capability supporting self-service and distributed data and analytics consumers. 
• AI first data architecture, analytics, and data engineering where human written code and visualization development have been eliminated. 
• A single, trusted enterprise semantic layer adopted consistently across analytics, reporting, data integration, enterprise AI, and data products.
• Repeatable processes to identify and eliminate metric discrepancies and reconciliation effort across business units. 
• Measurable improvement in data quality, transparency, and trust in executive and regulatory reporting.
• Governance recognized as an enabler of speed, scale, and better business decisions. 
• Ensure data products are aligned with priority use cases and desired business outcomes