VP, Enterprise Data Management

Job Type Full-time Description

POSITION OVERVIEW

We are seeking a visionary and pragmatic leader to architect our enterprise data ecosystem and spearhead our analytics strategy from conception through execution. The ideal candidate is an experienced data leader who possesses a strong strategic vision coupled with demonstrable hands-on technical experience. This enterprise role is responsible for designing scalable data architecture, establishing foundational data governance and management practices, defining clear analytics priorities and standards, and building/leading a high-performing data team. You will be instrumental in transforming data into a trusted, accessible, and strategic asset that drives critical business decisions, operational efficiency, and innovation across the company, fostering a data-informed culture. This role reports to the Chief Technology Officer and will work closely with other business analytics consumers.

ESSENTIAL FUNCTIONS INCLUDE, BUT ARE NOT LIMITED TO

• Strategic Leadership & Roadmap: Develop, articulate, and drive the comprehensive enterprise data and analytics strategy and roadmap, ensuring alignment with overall business objectives. Champion a data-driven culture throughout the organization.
• Data Architecture Design & Oversight: Lead the design, implementation, and evolution of the enterprise data architecture, including data models, data warehousing/lake solutions, integration patterns, and technology platform selection (Cloud focus: AWS/Azure/GCP). Ensure architecture supports scalability, reliability, performance, and security.
• Governance Framework Implementation: Architect and operationalize data governance framework encompassing policies, standards, processes, data quality controls, metadata management, and master data management (MDM) principles. Ensure compliance with data privacy regulations (e.g., GDPR, CCPA) in collaboration with legal/security teams. Establish and chair a      Data Governance committee.
• Data Management Practices: Establish and enforce data lifecycle management, governance policies, standards, and procedures. Oversee the development and implementation of data quality monitoring and remediation, data definition standardization, and data lineage, and initiatives to ensure data accuracy, completeness, and consistency. Lead Master Data Management (MDM) and Reference Data Management strategies and implementations. Define and manage data security classifications, access controls, and privacy compliance (e.g., GDPR, CCPA) in collaboration with security and legal teams. Oversee data lifecycle management, including data retention and archival policies. 
• Data Visualization: Possesses strong data visualization skills to design, develop, and maintain insightful, actionable dashboards and reports using tools like Tableau or Power BI. This includes translating complex data into compelling visual stories, applying analytical interpretation to identify trends through visualization, adhering to design best practices for clarity and impact, and preparing data for accurate visual representation.
• Analytics Enablement & Prioritization: Develop and manage a transparent framework for prioritizing analytics initiatives based on business value. Define standards for key business metrics/KPIs and promote consistent reporting/analytics practices. Oversee the strategy for delivering actionable insights via BI tools and analytics techniques.
• Team Leadership & Development: Develop, mentor, and lead an enterprise team of data professionals (e.g., architects, engineers, analysts, stewards), fostering a collaborative and high-performance environment.

· Stakeholder Collaboration & Communication: Act as the primary point of contact for senior leadership and business units regarding data strategy, architecture, management, and analytics. Collaborate closely with IT infrastructure, application development, security, and business departments to understand their data needs and ensure alignment. Translate complex technical concepts into clear, understandable terms for non-technical stakeholders. Champion data literacy across the organization.

• Implement Data Management Practices: Oversee the implementation of data quality rules, metadata management, master data definitions, and data lineage tracking.
• Drive Standardization: Establish standard definitions for key business metrics and KPIs. Promote the adoption of standard analytics tools, technologies, and reporting practices where appropriate.
• Facilitate Cross-Functional Collaboration: Work closely with IT, data engineering, business units, and leadership to unders