The Vice President, Data & Analytics provides executive leadership for the organization's data strategy, data governance, enterprise data warehouse, business intelligence, and AI initiatives — and is a true player-coach: an executive who sets direction and builds the team while remaining hands-on in the platform working alongside the team they lead. This role ensures that data and AI are treated as strategic assets, enabling informed decision-making, operational excellence, and business growth.

Work You’ll Do:

Leadership & strategy

• Player-coach profile: leads a small, global team but personally engaged in deep technical work
• Strong documentation and knowledge-management habits — data contracts, table docs, and query patterns as first-class deliverables
• Vendor/platform cost management: compute governance, storage lifecycle policies, and experience managing the cost / value equation
• Partner with business and enterprise stakeholders to align data initiatives with broader organizational priorities
• Build, mentor, and retain a high-performing global team; set the operating model, hiring plan, and delivery priorities
• Establish enterprise data governance and stewardship — data ownership, quality standards, and access policy — partnering with security, legal, and compliance

Hands-on lakehouse engineering

• Expert-level Databricks (or similar technology): Delta Lake internals (MERGE/CDC patterns, OPTIMIZE, vacuum/retention, time travel), SQL warehouses, and workflow/job orchestration
• Deep experience with medallion (bronze/silver/gold) architectures — efficicent load design, dedup strategy at ingestion, and grain/key discipline through each layer
• Strong Unity Catalog governance skills: access model design, lineage, and system-table observability

Data architecture & modeling

• Proven dimensional-modeling depth (Kimball-style facts/dims): define and enforce fact grain, conformed dimensions, and surrogate-key discipline
• Multi-ERP integration experience (SAP, Dynamics, Navision, JDE/E1, IFS or similar)
• CDC/replication architecture: choosing and implementing change-capture patterns that minimize storage and compute requirements

Assessment & redesign capability

• Track record of leading a platform assessment and rationalization: data-quality profiling, source-to-target lineage reconstruction, and storage/compute cost reduction
• Capability to develop and stand up a continuous data-quality framework: automated grain/duplication/reconciliation tests in the pipelines (e.g., dbt tests, Delta Live Tables expectations, or equivalent)
• Financial reconciliation mindset: experience tying warehouse facts to reported financials (orders vs bookings vs GL) and documenting where they legitimately diverge
• Pragmatic migration planning: can sequence a redesign while keeping certified models and executive dashboards live

AI & advanced analytics

• Define and drive the AI/ML and generative-AI strategy: identify, prioritize, and sequence high-value use cases tied to measurable business outcomes
• Stand up responsible-AI and model governance: evaluation, monitoring, data-privacy, and risk controls for both predictive and generative systems
• Enable self-service analytics and citizen development through governed data products and a trusted semantic layer

Reporting:

Reporting to the Chief Enterprise Business Services Officer, the Vice President leads a team spanning data warehouse and AI, and partners with a much larger group of indirect stakeholders across the business.

Basic Qualifications:

• Bachelor’s degree inData Science, Information Systems, Computer Science, or related field — or equivalent experience.

Preferred Qualifications:

• 10 or more yearsof progressive experience inanalytics, business intelligence, and data administration
• Expertise in data governance, data architecture, BI platforms, and cloud data technologies,
• Strong proficiency with data modeling and analytics methodologies
• Strong executive communication and strategic planning skills
• Demonstrated people-leadership: building, mentoring, and retaining technical teams while remaining hands-on in the platform
• Experience setting AI/ML and analytics strategy and standing up data or AI governance at enterprise scale