Strategy and Operations Manager - AI-Native Business Operations

Job Summary

This role helps the leaders of NetApp's product organization decide where people and money go, and makes those decisions faster and better informed. It spans planning, forecasting and tracking the organization's people and resources, and the budget across headcount, open reqs, software, cloud and AI spend. On each of these the analyst brings a point of view, not just the numbers behind it.

The role combines three skillsets that usually live in three different people: the consultant who frames an ambiguous question and lands a recommendation with senior leaders; the analyst who builds the model and can defend every number in it; and the AI-first builder who turns recurring work into systems that run themselves. The unit of delivery is a better decision, made sooner, on numbers leaders trust.

The role sits within the AI-Native Business Operations team in the product organization, and reports to the Director, AI-Native Business Operations. The team works in close partnership with Finance, HR, Talent Acquisition and engineering leadership across the product organization.

A few principles shape how the team approaches its work:

• We start from outcomes, not from tasks. The first question on any piece of work is what it is for and what good looks like, not how to automate the current steps.
• One source of truth. People, plans and spend live in one trusted, connected source, and every report and dashboard draws from it.
• AI first, with human judgment throughout. AI shortens the time to an outcome; our judgment tests every assumption and validates each step, from the source data to the final recommendation.
• Accountability does not stop at the answer. The analyst stands behind the method and the data as well as the result.

Key Responsibilities

• Plan, forecast and track. Build the plans and forecasts for the product organization's people and resources, and track them month by month: where engineering capacity and spend go across products and investment themes, and how hiring, attrition and backfills move against plan.
• Manage the budget. Hold the product organization's spend against target with Finance, from headcount and open reqs to software, cloud and AI, and find where to consolidate, renegotiate or stop.
• Advise product leadership. Turn the numbers into a clear point of view for vice presidents and their staff, and run the planning cycles and leadership reviews where those decisions get made.
• Operationalize strategic initiatives. Take a leadership priority from idea to working program: define its scope, set milestones and checkpoints, build the plan and put it in motion, then measure the results and keep leadership updated as it lands.
• Automate the recurring work. Build agents, automations and dashboards with Claude and the team's other AI tools that make each cycle take less effort than the last.
• Keep the source of truth trustworthy. Maintain the connected data behind every leadership view, with the checks and audit trail that let a leader defend any figure in front of Finance or the executive staff.
• Work across functions. Partner with Finance, HR, Talent Acquisition, IT and data owners on access, definitions and changes, within NetApp standards for privacy and acceptable use.

Education

• Typically requires a minimum of 8 years of related experience with a Bachelor's degree or equivalent professional experience, including a minimum of 3 years in consulting. This experience may have been gained in:

• • The strategy, operations or analytics practice of a management consulting firm, such as McKinsey, BCG, Bain, Deloitte, or a comparable practice
  • A business operations, strategy and operations, or FP&A team at an AI-first technology company
  • A founding or early operating role at a startup building AI-native products
  • Uses AI as the default way of working, across analysis, writing and building, with examples to show, and builds validation into each step so the speed never costs accuracy. Familiarity with Claude Code or Cursor is preferred.
  • Learns new domains, systems and data quickly, and acts without waiting for a perfect brief; comfortable where the problem is still being defined.
  • Expert scenario and financial modeling: headcount, cost and what-if models that leaders use to make decisions, and the judgment to spot when a number is wrong.
  • A technical, hands-on approach to data: reads data architectures and table schemas, writes the joins in SQL or Python, and treats data preparation as part of the job, from cleaning and de-duplicating records to validating formats and keys before any number is used. Experience with workforce or finance data in systems such as Workday or Snowflake is a plus.
  • A track record of influencing directors and vice presidents without authority, with recommendations that were acted on.
  • Excellent written and verbal communication, with the ability to disagree productively and adjust as evidence evolves.