Group Product Manager, Enterprise Data

Intuit is reimagining how enterprise data is discovered, understood, governed, and consumed in an agent-first world. Historically, data platforms have optimized for expert users navigating schemas, tools, pipelines, and access processes. As AI agents become a primary interface for analytics, product experiences, decisioning, and automation, those legacy workflows are no longer sufficient. Our opportunity is to redesign the enterprise data experience from the ground up.

Within Data Foundations, we are building an enterprise context layer that gives every application, user, and AI agent access to clean, connected, governed business context without requiring them to understand the underlying complexity of where data lives or how it is modeled. At the center of this strategy is the Business Object Model , which establishes canonical definitions for Intuit’s most important business concepts and relationships. Around that foundation, we are creating reusable agent context, trusted and quality-controlled data, semantic relationships, metadata, lineage, and policy-aware access patterns that can be consumed consistently across Intuit.

Our ambition is to make the default experience. The Group Product Manager for Enterprise Data, Context Layer & Agent-First Data Consumption will lead this transformation. This leader will define how Intuit builds the context layer that sits between foundational enterprise data and the agents, applications, analysts, and customer experiences that depend on it. The role spans product strategy for the Business Object Model, agent-ready context, governed consumption, self-service discovery and access, and the paved paths that make these capabilities the easiest and fastest way to build at Intuit.

The opportunity is larger than modernizing a data platform. It is to establish the enterprise context architecture for Intuit’s agentic future .

Responsibilities

- Define the multi-year product vision, strategy, and roadmap for Intuit’s Enterprise Context Layer and Agent-First Data Consumption capabilities.
- Lead transformation of agent led governance, data access, data processing and create new data science paved paths
- Lead the transformation from legacy, human-centric data workflows toward agent-first, intent-driven, self-service consumption experiences.
- Establish the product strategy for the enterprise context layer that provides applications and AI agents with trusted business meaning, relationships, metadata, lineage, quality, and policy-aware access.
- Partner with engineering and architecture to make clean, connected enterprise data available through reusable, scalable interfaces designed for both human and machine consumers.
- Define how reusable agent context is created, governed, composed, retrieved, and shared across multiple AI agents and product experiences.
- Establish product requirements for agent-consumable semantics, relationships, metadata, lineage, data quality, and policy signals.
- Reimagine existing discovery, access, reconciliation, onboarding, and integration workflows to remove unnecessary human intervention and engineering dependency.
- Lead the evolution from manual and ticket-driven access processes toward fine-grained, automated, policy-aware access control.
- Partner across privacy, security, legal, data governance, and engineering to embed authorization, purpose, consent, and permitted-use policies directly into the context and consumption experience.
- Establish clean-data expectations and product mechanisms for quality, certification, freshness, provenance, and trust within the context layer.
- Translate complex customer requirements, business semantics, technical constraints, and governance requirements into reusable platform capabilities.
- Build deep partnerships with product, AI, analytics, data science, and engineering teams to identify high-value agentic and data consumption use cases.
- Own the cross-portfolio GTM and customer success strategy required to drive adoption of context-layer and data-consumption capabilities.
- Partner closely with senior engineering and architecture leaders to make explicit tradeoffs across abstraction, extensibility, performance, reliability, governance, and customer experience.
- Recruit, lead, coach, and develop a high-performing team of product managers.

Qualifications

- 10+ years of product management experience across enterprise data platforms, data infrastructure, analytics, AI, developer platforms, or related technical products.
- Demonstrated success leading platform transformations that replaced fragmented or bespoke workflows with reusable enterprise capabilities.
- Strong understanding of modern enterprise data architecture, including data modeling, metadata, governance, data quality, lineage, and data consumption patterns.
- Experience defining platform abstractions and paved paths that hide infrastructure complexity while preserving flexibility for sophisticated users.
- Strong understanding of semantic data concepts, including canonical models, business entities, semantic layers, or comparable enterprise modeling approaches.
- Experience building or leading products consumed by developers, analysts, data scientists, AI systems, or other technical customers.
- Strong understanding of AI and agentic architectures, including how agents discover, retrieve, interpret, and act on enterprise context.
- Ability to define product requirements for machine-consumable context, metadata, semantics, permissions, and governance.
- Experience with enterprise authorization, access control, policy enforcement, data privacy, or governance systems.
- Strong customer orientation with demonstrated ability to convert complex and conflicting customer requirements into scalable platform solutions.
- Demonstrated success driving adoption and migration from legacy systems or workflows to new platform capabilities.
- Experience leading, recruiting, coaching, and developing product managers.
- Strong executive communication skills and the ability to simplify complex technical and strategic concepts for diverse audiences.

Preferred

- Experience with Databricks, Snowflake, AWS data services, or comparable modern data platforms.
- Experience with knowledge graphs, ontologies, semantic layers, enterprise business object models, entity resolution, or canonical data models.
- Experience with fine-grained access control, policy engines, consent, purpose-based access, or automated data governance.

Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit®: Careers | Benefits ). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.
The expected base pay range for this position is:
Mountain View $237,500 - $321,500