Principal, Value Management

Cloudera•US-Texas-Remote, MO

3d•Remote

About The Position

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. As a Principal in Business Value Management, you operate at the intersection of business value, AI platform architectures, and deal strategy. You partner with account teams and customer executives to translate complex data and AI capabilities into quantified economic outcomes, shaping investment decisions, influencing deal structures, and enabling enterprises to transition toward AI-native operating models. This is an individual contributor role.

Requirements

5–7+ years in strategy consulting, value engineering, or equivalent customer-facing roles
Strong commercial and financial acumen, including advanced financial modeling (DCF, NPV, IRR) and the ability to link technology decisions to P&L, balance sheet, and cash flow outcomes
Demonstrated ability to influence enterprise deal cycles, including shaping deal strategy, positioning, and commercial outcomes
Solid understanding of modern data platforms, distributed systems, and AI/ML workloads, including data pipelines, model deployment, and infrastructure trade-offs
Ability to quantitatively reason about evolving cost drivers in AI and data environments, including compute, storage, and workload patterns
Hands-on experience using AI tools (e.g., LLMs, automation frameworks) to support analysis, modeling, and content generation
Strong executive presence with the ability to engage and influence CIO, CFO, CDO, and senior business stakeholders
Proven experience working closely with Sales teams in complex, high-value enterprise deals
Excellent communication skills, with the ability to distill complex technical and financial concepts into clear, actionable insights
High ownership mindset, with the ability to operate independently in ambiguous, high-growth environments

Nice To Haves

Post-MBA experience, particularly within a top-tier management consulting firm or equivalent environment
Experience designing or deploying enterprise AI or data platform architectures in production environments
Deep expertise in one or more industries such as Financial Services, Manufacturing, Consumer Goods, or Telecommunication
Experience building scalable value engineering assets such as TCO calculators, benchmarking models, or scenario simulators
Familiarity with hybrid and on-prem AI deployments, including cost and performance trade-offs versus public cloud

Responsibilities

Drive ARR growth by embedding quantified, forward-looking value narratives into deal strategy, linking AI platform architectures and emerging AI operating models to revenue uplift, cost optimization, and risk reduction.
Engage C-suite stakeholders to shape enterprise AI and data strategies, positioning Cloudera within broader shifts toward platform consolidation, hybrid architectures, and AI-enabled business models.
Build and present CFO-grade business cases (TCO, ROI, NPV, IRR), incorporating evolving cost dynamics such as inference workloads, utilization patterns, and long-term AI operating economics.
Lead value discovery engagements to map current-state environments to future-state AI-native architectures, with explicit quantification of value drivers, investment trade-offs, and execution pathways.
Support complex, multi-year deal cycles through commercial structuring, scenario modeling, and competitive positioning against cloud-native and emerging AI platform alternatives.
Align solution design with commercial objectives by partnering with Sales, Solutions Engineering, and Product, ensuring architectures are optimized for performance, scalability, and durable economic efficiency in AI-driven environments.
Represent Cloudera in executive forums, articulating perspectives on enterprise AI economics, architectural trade-offs, and the long-term implications of AI adoption on cost structures and operating models.
Leverage AI tools and automation, including LLMs and agentic workflows, to scale analysis, accelerate insight generation, and industrialize repeatable assets such as TCO models and scenario simulators.