Vice President, Data & Analytics

Hillenbrand•Columbus, OH

About The Position

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.

Requirements

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

Nice To Haves

10 or more years of progressive experience in analytics, business intelligence, and data administration

Responsibilities

Provides executive leadership for the organization's data strategy, data governance, enterprise data warehouse, business intelligence, and AI initiatives.
Sets direction and builds the team while remaining hands-on in the platform working alongside the team they lead.
Ensures that data and AI are treated as strategic assets, enabling informed decision-making, operational excellence, and business growth.
Leads a small, global team but personally engaged in deep technical work.
Maintains strong documentation and knowledge-management habits — data contracts, table docs, and query patterns as first-class deliverables.
Manages vendor/platform costs: compute governance, storage lifecycle policies, and experience managing the cost / value equation.
Partners with business and enterprise stakeholders to align data initiatives with broader organizational priorities.
Builds, mentors, and retains a high-performing global team; sets the operating model, hiring plan, and delivery priorities.
Establishes enterprise data governance and stewardship — data ownership, quality standards, and access policy — partnering with security, legal, and compliance.
Performs expert-level Databricks (or similar technology) engineering: Delta Lake internals (MERGE/CDC patterns, OPTIMIZE, vacuum/retention, time travel), SQL warehouses, and workflow/job orchestration.
Implements deep experience with medallion (bronze/silver/gold) architectures — efficient load design, dedup strategy at ingestion, and grain/key discipline through each layer.
Utilizes strong Unity Catalog governance skills: access model design, lineage, and system-table observability.
Applies proven dimensional-modeling depth (Kimball-style facts/dims): defines and enforces fact grain, conformed dimensions, and surrogate-key discipline.
Manages multi-ERP integration experience (SAP, Dynamics, Navision, JDE/E1, IFS or similar).
Designs CDC/replication architecture: choosing and implementing change-capture patterns that minimize storage and compute requirements.
Leads platform assessment and rationalization: data-quality profiling, source-to-target lineage reconstruction, and storage/compute cost reduction.
Develops and stands up a continuous data-quality framework: automated grain/duplication/reconciliation tests in the pipelines (e.g., dbt tests, Delta Live Tables expectations, or equivalent).
Performs financial reconciliation: experience tying warehouse facts to reported financials (orders vs bookings vs GL) and documenting where they legitimately diverge.
Develops pragmatic migration plans: sequences a redesign while keeping certified models and executive dashboards live.
Defines and drives the AI/ML and generative-AI strategy: identifies, prioritizes, and sequences high-value use cases tied to measurable business outcomes.
Stands up responsible-AI and model governance: evaluation, monitoring, data-privacy, and risk controls for both predictive and generative systems.
Enables self-service analytics and citizen development through governed data products and a trusted semantic layer.