Manager, Applied AI Strategy and Operations

Business Area:

Corp. Strategy

Seniority Level:

Mid-Senior level

Job Description:

At Cloudera, we empower people to transform complex data into clear and actionable insights. With as much data under management as the hyperscalers, we’re the preferred data partner for the top companies in almost every industry.  Powered by the relentless innovation of the open source community, Cloudera advances digital transformation for the world’s largest enterprises.

Job title: Manager, Applied AI Strategy and Operations

Job Purpose:

As a  Manager, Applied AI Strategy and Operations within the Chief Business Officer and GM, Applied AI’s  organization, you will lead high-impact, global commercial and operational initiatives with a direct emphasis on accelerating Cloudera’s Private AI growth.

Our Applied AI organization is a fast-moving, dedicated group of Forward Deployed Engineers (FDEs), Industry & Applied AI Specialists, and business strategy leaders committed to establishing Cloudera as the trusted standard for Private AI in the enterprise. In this high-autonomy, high-trust role, you will act as the strategic and analytical backbone behind our field engineering motions. You will prioritize the right initiatives across our strategic accounts, establish robust governance and operating rhythms, conduct deep-dive portfolio and sales pipeline analyses, and synthesize complex technical and commercial signals into actionable recommendations for Cloudera’s senior leadership.

Key responsibilities:

• Drive Global Governance & Operating Rhythms: Establish, evolve, and manage the programmatic operating rhythm for the global Applied AI organization across AMER, EMEA, and APAC. Lead the monthly business review (MBR) process—from metric gathering and CRM ingestion through deep quantitative analysis and executive synthesis for senior leadership.
• Manage the AI Adoption Funnel: Partner with regional FDE and Applied AI Specialist leadership to track, analyze, and optimize customer progression through our four core execution stages: Discovery & Strategy, Solution & Value, Pilot & Prove, and Production & Scale.
• Account Prioritization & Use Case Frameworks: Structure analytical frameworks grounded in real customer signal—joining technical discovery sessions, reviewing triage outcomes, and working directly with field engineering teams to filter and isolate high-ROI Private AI workloads. Conduct rigorous analyses to down-select and prioritize target accounts across our Top 100 strategic enterprise lists.
• Define & Standardize Strategic Metrics: Collaborate with central Revenue Operations and AI GTM Sales Strategy to define, refine, and standardize key performance indicators around subscription and consumption-based Private AI adoption. Track organizational progress against critical milestones, including customer pitch meetings, on-site Use Case Workshops, Applied AI Hands-on Labs, and deployed production solutions.
• Ecosystem & Commercial Strategy: Analyze and support go-to-market operational alignment with major AI infrastructure and ecosystem partners (including NVIDIA Blueprints and VAST). Evaluate revenue share models, joint sales plays, and co-deployment strategies to maximize platform pull-through and annual recurring revenue (ARR) expansion.
• Cross-Functional Leadership & Solution Productization: Act as an embedded, highly trusted thought partner to technical and commercial leaders. Design and implement scalable processes for tracking team allocations and solution productization, ensuring repeatable delivery patterns are codified and communicated across Account Executives, Solution Engineers, and Professional Services globally.
• Business Planning & Executive Storytelling: Build comprehensive segment analyses, growth models, and strategic business cases to support critical investment decisions, headcount planning, and resource allocation across regional pods.

Preferred Qualifications:

• Strong problem-solving and structuring skills, with a proven ability to scope complex, ambiguous strategic challenges and identify root causes within technical go-to-market organizations.
• Highly analytical and excellent at leveraging facts, CRM data, and customer insights to generate and validate strategic hypotheses regarding enterprise AI adoption and workload consumption.
• Proven ability to communicate and build trust with stakeholders at all levels—including C-suite executives, technical engineering leaders, and …