Director, Data & AI

NetApp, Inc.•Durham, NC

7d•$196,350 - $292,600•Remote

About The Position

As Director, Data & AI readiness you will own the strategy to enable data & intelligence layer powering NetApp’s most critical corporate functions — Sales, HR, Finance, Tax & Treasury, Customer Support, and Professional Services This is an opportunity to lead a major data transformation program — enabling tools, technology, data and context for AI across enterprise applications and data platforms for AI; — while simultaneously upskilling the team for the AI-native era and embedding modern data management practices at scale. You will own the forward-looking data science strategy, define and execute the roadmap, and enable self-service for advanced business users. You will lead through influence in a global organization, partnering with functions and will personally coach the team to deliver deep analytical insight projects that drive measurable business value outcomes. To be successful, you must bring extensive enterprise data & AI leadership experience, product lifecycle fluency (including SDLC), a track record of implementing cutting edge large-scale technology, rich experience in AI/ML and the credibility to be a trusted adviser to senior executives in a high-growth environment.

Requirements

10+ years of progressive experience in data, analytics, business intelligence and AI/ML
2+ years' experience in enabling enterprises to leverage data to accelerate AI adoption
Proven track record delivering enterprise analytics and Agentic AI with measurable business impact within Finance, HR, Customer Operations, or Professional Services environments
Technical depth in modern data architecture — cloud data platforms (Snowflake, Databricks, BigQuery, or equivalent), semantic modeling, knowledge graph, MCP, ETL/ELT pipelines, and BI tooling (Tableau, Power BI, MicroStrategy, or equivalent)
Applied AI and machine learning experience — hands-on background with AI agent development, orchestration, and lifecycle management; familiarity with LLM-based solutions and predictive modeling
Executive stakeholder management — demonstrated ability to influence VP and C-suite audiences, drive consensus, and align diverse stakeholders around a shared data and AI vision
Program and budget management — skilled in managing complex, multi-workstream programs with OKRs tied to business outcomes; experienced in vendor negotiation, contract management, and ROI tracking for analytics and AI investments
Bachelor' / Master’s degree in computer science, Data Science, Statistics, Information Systems, Business Analytics, or a related field (or equivalent practical experience)

Nice To Haves

Strategic vision — you connect today’s data investments to tomorrow’s business outcomes; you think in systems and programs, not isolated projects
Executive influence and storytelling — you build credibility with senior leaders by crafting compelling narratives from complex data; your clarity moves people to act
Change leadership — you have led organizational transformation before; you know how to bring skeptics along, build coalitions, and make AI adoption stick
People development — you grow careers intentionally; your team members leave every conversation more capable and more motivated than when they arrived
Metrics and impact orientation — you define and track KPIs and OKRs that link analytics initiatives to measurable business impact; you hold yourself and your team to that standard

Responsibilities

Develop and execute a comprehensive business intelligence and AI/ML strategy, roadmap aligned with NetApp’s business model and organizational goals — setting objectives and key results linked to revenue, retention, efficiency, and customer experience across HR, Finance, Tax & Treasury, Customer Support, and Professional Services
Build and run AI/ML Center of excellence establishing standards, reusable assets, and a delivery model that scales data & AI/ML products consistently across corporate functions
Champion adoption — own change management and executive stakeholder alignment, driving the mindset and capability shifts needed for NetApp to operate as an AI-native organization
Lead through influence in a global, matrixed structure — building trusted partnerships with function leaders in HR, Finance, Tax & Treasury, Customer Support, and Professional Services who do not directly report to you
Stand up Data & tools to support agentic workflows — e.g., workforce insights, financial forecast hygiene, customer support deflection, account next-best-action — and maintain full lifecycle management including versioning, monitoring, and rollback
Partner with business to Identify & prioritize AI outcomes dependent on Data across HR analytics, financial planning, tax data automation, customer support automation, and PS margin optimization; build the business case and sequencing for each
Oversee the deployment, governance, and lifecycle management of data, BI and AI/ML agents and solutions, ensuring they are auditable, explainable, and aligned to NetApp’s responsible AI standards
Lead the development and maintenance of the enterprise data domain model, taxonomy, and ontologies to ensure shared understanding, semantic consistency, and discoverability of data and knowledge assets.
Design and evolve information and semantic models that make enterprise data AI‑ready, supporting use cases ranging from traditional analytics and BI to applied machine learning and LLM‑based experiences (e.g., search, retrieval‑augmented generation, and copilots).
Operationalize data models, taxonomies, and semantic structures through the Enterprise Data Catalog
Drive definition and enforce standards for data modeling, taxonomy, nomenclature, and semantic structures to ensure consistency and interoperability across business domains and downstream consumption patterns.
Contribute to the enterprise data product framework by defining domain boundaries, shared dimensions, and semantic contracts that enable cross‑domain interoperability and AI consumption.
Partner with analytics & AI engineering teams to ensure information architecture, metadata, and semantic context are sufficient to support explainable, governed, and trustworthy AI outcomes.
Serve as a thought partner, provide insights from modeling, catalog adoption, and AI enablement to shape governance strategy and roadmaps.
Revamp data governance tooling — partnering with IT and Data Engineering to create and maintain data governance tools and capabilities required for data governance organization
Develop the strategy and tools to enable ontology and semantic
Implement and govern a responsible framework driving enablement of fairness, privacy, transparency, human oversight, risk reporting, and periodic audits; own ethical AI practices including bias mitigation, model transparency, and remediation planning
Ensure compliance with global privacy and data regulations (GDPR, CCPA, PIPEDA, and equivalents) across all data products and AI solutions