VP, Data Strategy & Architecture at RxBenefits

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Full Time

Start Date

Immediate

Expiry Date

22 Jun, 26

Salary

0.0

Posted On

24 Mar, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Strategy, Data Architecture, Data Governance, Data Management, Analytics, Artificial Intelligence, Digital Experiences, Roadmap Definition, Data Platforms, Semantic Layer, Data Quality, Metadata, Lineage, Privacy Policies, Strategic Planning, Cross-functional Leadership

Industry

Insurance and Employee Benefit Funds

Description

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. 4. Analytics, BI and AI Enablement * Build a pragmatic operating model optimizing Reporting, Analytics, and Data Science delivery * Mature business intelligence, reporting and advanced analytics capabilities supporting distributed and self-service model. * Operationalize AI/ML use cases for predictive analytics and advancing creation of common enterprise semantic layer. * Ensure accuracy of all reporting and dashboards and push data democratization for business consumption via right tools and data literacy. 5. 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

Responsibilities

This executive will define the enterprise data direction, target-state architecture, and operating model to enable scalable analytics, AI, and digital experiences. Key duties include translating business needs into a roadmap for data platforms, integration patterns, and establishing robust data governance, stewardship, and quality controls.