Director, Data & AI
for each heading. Use - for any list of duties/requirements/benefits, and
for prose. Do NOT repeat job title as heading. Do NOT put Job ID/requisition numbers as headings. Remove boilerplate. Include only sections when posting has real specific content. Must preserve all substantive info. No invented or summarized away detail. No filler like "commensurate with experience". Must not create Pay/Schedule/Benefits sections unless posting actually states them. The posting includes Compensation: target salary range, and mentions benefits package. So we can include Pay section with salary range. Benefits: mention comprehensive benefits package (health insurance, life insurance, retirement, PTO, etc). Schedule: mentions hybrid working environment, some level of in-office/in-person expectations. So we can include Schedule section.
We need sections: About the role (job summary), Responsibilities, Requirements (maybe combine job requirements and leadership competencies?), Qualifications (education?), Skills (technical depth?), Benefits, Pay, Schedule.
We must not create sections with filler. Ensure each has substantive content.
We need to extract content.
Let's parse:
Job Summary: "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."
That's About the role.
Responsibilities: bullet list of many items. We'll list each as
- .
Requirements: "Job Requirements" bullet list: 10+ years progressive experience..., 2+ years experience enabling enterprises..., Proven track record delivering enterprise analytics and Agentic AI..., Technical depth..., Applied AI..., Executive stakeholder management..., Program and budget management..., Bachelor's/Master's degree...
Also Leadership Competencies: could be part of Requirements or separate Skills? Probably Skills section for leadership competencies. But we have Skills section; we can put leadership competencies there.
Qualifications: Education: "Education IC - Typically requires a minimum of 15 years of related experience.Mgr & Exec - Typically requires a minimum of 12 years of related experience." That seems odd; maybe it's about IC vs Mgr & Exec levels. Could be considered Qualifications? Might be redundant with experience. We could treat as Qualifications (education/experience). But we already have experience in Requirements. Might be better to put Education under Qualifications. However the text: "Education IC - Typically requires a minimum of 15 years of related experience.Mgr & Exec - Typically requires a minimum of 12 years of related experience." This seems like a note about experience levels for different tracks. Might be considered Qualifications. We'll include as a short paragraph.
Skills: technical depth list: cloud data platforms (Snowflake, Databricks, BigQuery, or equivalent), semantic modeling, knowledge graph, MCP, ETL/ELT pipelines, BI tooling (Tableau, Power BI, MicroStrategy, or equivalent). Also applied AI/ML experience: hands-on background with AI agent development, orchestration, lifecycle management; familiarity with LLM-based solutions and predictive modeling. Also leadership competencies: strategic vision, executive influence and storytelling, change leadership, people development, metrics and impact orientation.
We can combine technical skills and leadership competencies under Skills.
Benefits: "This may cover Health Insurance, Life Insurance, Retirement or Pension Plans, Paid Time Off, various Leave options, employee stock purchase plan, and/or restricted stocks (RSU’s)." Also "comprehensive benefits package". We'll list.
Pay: "Compensation: The target salary range for this position is 196,350 - 292,600 USD. The salary offered will be determined by the candidate's location, qualifications, experience, and education and may be outside of this range. The range is based on 'On Target Earnings’ (OTE) representing the total potential earnings, which is the sum of the base salary and potential commission earned when performance targets are achieved. Final compensation packages are competitive and in line with industry standards, reflecting a variety of factors, and include a comprehensive benefits package."
Schedule: "At NetApp, we embrace a hybrid working environment designed to strengthen connection, collaboration, and culture for all employees. This means that most roles will have some level of in-office and/or in-person expectations, which will be shared during the recruitment process."
We must not include boilerplate like EEO, equal opportunity, scam warning, application instructions, etc. Also remove "Why You'll Thrive at NetApp", "Apply now.", "Submitting an Application", "AI Disclosure". Also remove "At NetApp, your ideas power innovation." etc? That's part of intro but maybe considered fluff but still substantive? It's marketing but not boilerplate. However we need to preserve ALL substantive information; but we can consider that as part of About the role? The intro is generic but maybe we can keep as part of About the role? The instruction: "Prefer a few substantive sections over many thin ones." We could combine intro into About the role paragraph.
We must not repeat job title as heading. Already not.
We must not put Job ID/requisition numbers as headings (none present).
We must output only HTML fragment.
Let's craft:
About the role
... (job summary paragraph)
Responsibilities
- ...
...
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's / Master’s degree in computer science, Data Science, Statistics, Information Systems, Business Analytics, or a related field (or equivalent practical experience)
Qualifications
Education IC - Typically requires a minimum of 15 years of related experience. Mgr & Exec - Typically requires a minimum of 12 years of related experience.
Skills
- Strategic vision — connect today’s data investments to tomorrow’s business outcomes; think in systems and programs, not isolated projects
- Executive influence and storytelling — build credibility with senior leaders by crafting compelling narratives from complex data; clarity moves people to act
- Change leadership — led organizational transformation; bring skeptics along, build coalitions, and make AI adoption stick
- People development — grow careers intentionally; team members leave every conversation more capable and more motivated
- Metrics and impact orientation — define and track KPIs and OKRs that link analytics initiatives to measurable business impact; hold yourself and team to that standard
Benefits
- Health Insurance
- Life Insurance
- Retirement or Pension Plans
- Paid Time Off
- Various Leave options
- Employee stock purchase plan
- Restricted stocks (RSU’s)
Pay
The target salary range for this position is $196,350 - $292,600 USD. The salary offered will be determined by the candidate's location, qualifications, experience, and education and may be outside of this range. The range is based on 'On Target Earnings’ (OTE) representing the total potential earnings, which is the sum of the base salary and potential commission earned when performance targets are achieved.
Schedule
NetApp embraces a hybrid working environment; most roles have some level of in‑office and/or in‑person expectations, which will be shared during the recruitment process.
Check for any leftover boilerplate: we removed EEO, etc. Ensure no mention of "Equal Opportunity Employer". Good.
Check that we didn't invent or summarize away detail. We preserved all specifics. The responsibilities list we need to include all items from the responsibilities section. Let's extract them precisely.
Responsibilities list from posting:
- 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
for prose. Do NOT repeat job title as heading. Do NOT put Job ID/requisition numbers as headings. Remove boilerplate. Include only sections when posting has real specific content. Must preserve all substantive info. No invented or summarized away detail. No filler like "commensurate with experience". Must not create Pay/Schedule/Benefits sections unless posting actually states them. The posting includes Compensation: target salary range, and mentions benefits package. So we can include Pay section with salary range. Benefits: mention comprehensive benefits package (health insurance, life insurance, retirement, PTO, etc). Schedule: mentions hybrid working environment, some level of in-office/in-person expectations. So we can include Schedule section. We need sections: About the role (job summary), Responsibilities, Requirements (maybe combine job requirements and leadership competencies?), Qualifications (education?), Skills (technical depth?), Benefits, Pay, Schedule. We must not create sections with filler. Ensure each has substantive content. We need to extract content. Let's parse: Job Summary: "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." That's About the role. Responsibilities: bullet list of many items. We'll list each as
About the role
... (job summary paragraph)
Responsibilities
- ... ...
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's / Master’s degree in computer science, Data Science, Statistics, Information Systems, Business Analytics, or a related field (or equivalent practical experience)
Qualifications
Education IC - Typically requires a minimum of 15 years of related experience. Mgr & Exec - Typically requires a minimum of 12 years of related experience.
Skills
- Strategic vision — connect today’s data investments to tomorrow’s business outcomes; think in systems and programs, not isolated projects
- Executive influence and storytelling — build credibility with senior leaders by crafting compelling narratives from complex data; clarity moves people to act
- Change leadership — led organizational transformation; bring skeptics along, build coalitions, and make AI adoption stick
- People development — grow careers intentionally; team members leave every conversation more capable and more motivated
- Metrics and impact orientation — define and track KPIs and OKRs that link analytics initiatives to measurable business impact; hold yourself and team to that standard
Benefits
- Health Insurance
- Life Insurance
- Retirement or Pension Plans
- Paid Time Off
- Various Leave options
- Employee stock purchase plan
- Restricted stocks (RSU’s)
Pay
The target salary range for this position is $196,350 - $292,600 USD. The salary offered will be determined by the candidate's location, qualifications, experience, and education and may be outside of this range. The range is based on 'On Target Earnings’ (OTE) representing the total potential earnings, which is the sum of the base salary and potential commission earned when performance targets are achieved.
Schedule
NetApp embraces a hybrid working environment; most roles have some level of in‑office and/or in‑person expectations, which will be shared during the recruitment process.
Check for any leftover boilerplate: we removed EEO, etc. Ensure no mention of "Equal Opportunity Employer". Good. Check that we didn't invent or summarize away detail. We preserved all specifics. The responsibilities list we need to include all items from the responsibilities section. Let's extract them precisely. Responsibilities list from posting: - 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