Senior Staff Product Manager, Enterprise Data Platform

Intuit is reimagining how enterprise data is discovered, moved, processed, governed, and consumed in an agent-first world.

Historically, enterprise data platforms have been designed for technical experts navigating complex infrastructure, pipelines, schemas, tools, and access processes. As AI agents become a primary interface for analytics, data engineering, data science, decision-making, and automation, these traditional workflows are no longer sufficient.

Our opportunity is to fundamentally transform how data work gets done.

Within Intuit's Data Foundations organization, we are building the next generation of AI-native enterprise data platforms that make trusted, connected, governed data available to every application, developer, data scientist, analyst, and AI agent. Our platforms power enterprise-scale data movement, batch and real-time processing, data reliability, and self-service consumption, while abstracting away the underlying infrastructure complexity.

We are evolving these foundational capabilities into intelligent, agent-driven experiences that enable customers to express intent rather than manually orchestrate infrastructure. From discovering and accessing data to building pipelines, processing information, resolving failures, and producing trusted datasets, our ambition is to make complex data workflows increasingly autonomous, reliable, and accessible.

We are looking for a Senior Staff Product Manager, Enterprise Data Platform , who is energized by building AI-native platform products at enterprise scale.

We are particularly interested in product leaders with deep expertise in enterprise data processing platforms, context layers for data developers and data science teams, and secure, scalable data access patterns for AI agents . You will work at the intersection of enterprise data infrastructure, AI agents, developer experiences, governance, and platform architecture. You will build deep customer empathy, translate complex technical challenges into intuitive product experiences, and partner across engineering, architecture, security, governance, and business units to deliver measurable outcomes.

Our ambition is to make enterprise data workflows faster to build, governed by default, and continuously optimized at scale.

Responsibilities

Product Strategy, Innovation, and Execution

• Develop and execute a multi-year, AI-forward product strategy for Intuit's Enterprise Data Platform, aligned with business objectives, customer needs, and the evolution toward agent-first data experiences.

• Own the product strategy and measurable outcomes across three North Star pillars: Developer Productivity, Governance, and Platform Efficiency , ensuring major product investments deliver demonstrable improvements in these dimensions.

• Identify opportunities to fundamentally simplify platform experiences across data processing, data access, data lake infrastructure, and consumption, with a clear perspective on where AI delivers differentiated value versus traditional automation.

• Survey the enterprise data ecosystem, modern data architectures, AI/ML landscape, and emerging agent capabilities to identify the next wave of platform innovation and translate those opportunities into actionable product investments.

• Translate long-term product strategy into a prioritized roadmap that balances AI-native innovation with measurable improvements in developer experience, governance effectiveness, reliability, and platform economics.

• Define platform abstractions and reusable paved paths that simplify complex infrastructure while preserving the flexibility, performance, and extensibility required by sophisticated enterprise customers.

• Partner with engineering and applied AI/ML teams to design intelligent product experiences across enterprise data movement, batch processing, real-time streaming, data transformation, and analytical workflows.

• Establish a clear product definition and strategy for an AI-ready Enterprise Data Platform , including the context, metadata, semantic relationships, interfaces, and governed access patterns required to enable AI agents to discover, understand, and interact with enterprise data.

• Drive innovation in reusable enterprise context layers and agent-consumable capabilities that enable developers, data scientists, applications, and AI agents to access trusted data without navigating underlying platform complexity.

• Identify opportunities to replace manual, configuration-heavy workflows with agent-first, intent-driven experiences across pipeline authoring, deployment, monitoring, troubleshooting, governance, and data consumption.

Customer Obsession and Platform Adoption

• Lead the Customer Advisory Board and develop deep customer understanding across software engineers, data engineers, data scientists, ML engineers, analysts, and teams developing AI agents.

• Identify friction across the end-to-end data lifecycle, including onboarding, discovery, access, pipeline authoring, deployment, monitoring, troubleshooting, and data consumption.

• Establish clear baselines, success metrics, and feedback mechanisms to continuously measure improvements in developer productivity, governance automation, platform adoption, infrastructure utilization, processing performance, and cost efficiency.

Cross-Functional Leadership and Business Impact

• Operate as a senior individual contributor and strategic product leader, influencing technical direction and product priorities across multiple engineering organizations and business units.

• Build strong partnerships with engineering, architecture, AI/ML, security, privacy, governance, and infrastructure leaders to align on product priorities, architectural direction, and execution.

Qualifications

• 10+ years of product management experience delivering enterprise platforms, data infrastructure, developer platforms, AI/ML products, analytics capabilities, or related technical products.

• Demonstrated track record of defining product strategy and delivering complex, enterprise-scale platform capabilities that solve critical customer needs.

• Strong understanding of modern enterprise data architecture, including data ingestion, batch and streaming processing, transformation, orchestration, data quality, metadata, lineage, and governed data consumption.

• Strong understanding of AI-native and agentic architectures, including how AI agents discover, retrieve, interpret, and act on enterprise data and context.

• Strong product judgment in determining when AI agents, deterministic automation, or conventional software architectures are best suited to solve a problem.

• Demonstrated ability to define and deliver measurable platform outcomes across developer productivity, automated governance, and infrastructure efficiency, using clear metrics to guide product strategy, prioritization, and investment decisions.

• Ability to operate at multiple levels, from developing a compelling long-term product strategy to driving execution details and resolving technical tradeoffs.

• Demonstrated ability to lead through influence across engineering, architecture, design, security, governance, and other cross-functional teams.

• Experience driving platform adoption, customer migrations, and organizational change across complex enterprise environments.

Preferred

• Experience with Databricks, Apache Spark, Apache Flink, Kafka, AWS data services, Snowflake , or comparable enterprise data technologies.

• Experience building products for large-scale batch processing, stream processing, data orchestration, data movement, or managed compute infrastructure.

• Experience designing or delivering enterprise context layers, semantic data models, metadata-driven developer experiences, or reusable data access capabilities for data scientists and AI applications.

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