Senior Staff Product Manager - Tech Strategic Programs
Intuit · San Diego, CA · 1 mo ago
On-siteMarketing$206k–$278k/yrFull-time
We need to output HTML fragment with sections: Overview (maybe not a heading? The instruction: use for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). The posting includes Overview, Responsibilities, Qualifications, and then a paragraph about compensation and pay range. Also there is a line "Intuit provides a competitive compensation package..." and "Pay offered is based on factors..." and "The Expected Base Pay Range For This Position Is: Mountain View $205,500 - $278,000". So we need a Pay section with that info. Also maybe Benefits? Not mentioned beyond generic statement; but we should not create Benefits unless specific content. There's a generic statement about compensation package with cash bonus, equity rewards and benefits, but not specific benefits. The instruction: never create Pay/Schedule/Benefits sections unless the posting actually states them. It does state Pay (range) and mentions compensation package, but not specific benefits. Should we include Benefits? It says "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)." That's somewhat generic but does mention benefits. However the instruction says: never emit a heading followed by filler/placeholder text (e.g. "commensurate with experience", "not specified", "N/A", "varies", "TBD") and never create Pay/Schedule/Benefits sections unless the posting actually states them. It does state benefits generically, but not specific. Might be considered filler? Safer to not create Benefits section unless we have specific benefits listed. We have only generic mention; could be considered not specific enough. I'd omit Benefits.
We need sections: Overview (maybe as
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). The posting includes Overview, Responsibilities, Qualifications, and then a paragraph about compensation and pay range. Also there is a line "Intuit provides a competitive compensation package..." and "Pay offered is based on factors..." and "The Expected Base Pay Range For This Position Is: Mountain View $205,500 - $278,000". So we need a Pay section with that info. Also maybe Benefits? Not mentioned beyond generic statement; but we should not create Benefits unless specific content. There's a generic statement about compensation package with cash bonus, equity rewards and benefits, but not specific benefits. The instruction: never create Pay/Schedule/Benefits sections unless the posting actually states them. It does state Pay (range) and mentions compensation package, but not specific benefits. Should we include Benefits? It says "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)." That's somewhat generic but does mention benefits. However the instruction says: never emit a heading followed by filler/placeholder text (e.g. "commensurate with experience", "not specified", "N/A", "varies", "TBD") and never create Pay/Schedule/Benefits sections unless the posting actually states them. It does state benefits generically, but not specific. Might be considered filler? Safer to not create Benefits section unless we have specific benefits listed. We have only generic mention; could be considered not specific enough. I'd omit Benefits.
We need sections: Overview (maybe as Overview
) but the instruction says use for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). Overview is not in that list but we can still use heading? Probably yes, we can use Overview
. The instruction says use for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It doesn't forbid other headings, but we should follow similar pattern. We'll include Overview, Responsibilities, Qualifications, Pay.
We need to preserve all substantive information, not invent or summarize away detail. Use - for any list of duties/requirements/benefits. In Responsibilities there is a list of bullet points starting with "Own the product end to end." etc. Those are sentences each starting with a verb. We'll make each as
- . Also Qualifications has bullet points: "What you'll bring:" then a list of traits (Speed as a habit, Thought leadership..., etc). Then after that there is another "Qualifications" heading with more detailed qualifications (8+ years...). We need to combine? The posting repeats "Qualifications" heading twice: first a list of traits, then a second "Qualifications" with more detailed requirements. We should combine them under one Qualifications section, preserving all content. We'll make a
for the traits and another for the detailed requirements? Could be two lists. We'll keep them as separate under same heading.
We need to remove boilerplate: cookie/navigation/legal/EEO/equal-opportunity/scam-warning, duplicated lines, application instructions. There's none besides the compensation paragraph.
We need to output only HTML fragment, no /, no markdown fences.
Let's craft:
Overview
... (the overview paragraph).
Responsibilities
- Own the product end to end. Define and evolve the vision, strategy, and multi-quarter roadmap for the execution data platform and the AI experiences built on it — from data ingestion and entity resolution through agents, skills, briefings, and portfolio-level insights.
- Drive prioritization of data onboarding. Own the sequencing decisions that determine the platform's value: which data sources to integrate next, which programs and organizations to onboard, and what "good enough" data quality looks like at each stage. Make these calls transparently, with a clear framework and a defensible read of customer impact versus cost.
- Translate customer needs into requirements engineers can build from. Spend real time with program managers, goal owners, and technology leaders to understand their jobs-to-be-done. Turn messy, high-stakes workflows into problem statements, requirements, data contracts, and success metrics that hold up under scrutiny.
- Define durable, reusable capabilities. Expand individual business needs into capability strategies — ensuring cross-capability durability and AI reusability, so each new source, entity, or skill compounds rather than fragments. Prevent one-off builds from becoming the product.
- Partner deeply with engineering and architecture. Facilitate technical decisions and trade-offs with an AI-first mindset — retrieval and grounding strategy, entity resolution approach, freshness versus cost, agent orchestration, evaluation and quality bars. Hold a high bar for answers that are accurate, cited, and trustworthy.
- Own trust, privacy, and governance as product surface. Work with security, legal, privacy, and compliance partners to define access controls, tenancy isolation, consent, and auditability — and design the transparency experience that earns adoption incrementally rather than demanding it upfront.
- Set the metrics and manage to them. Define the customer benefit and business outcome metrics for the platform, instrument them, and use data, experimentation, and AI-assisted analysis to drive prioritization and demonstrate impact.
- Build the coalition. Influence and align product, engineering, program management, and business teams that each have their own roadmaps and incentives. Navigate the tension between long-term platform investment and near-term customer commitments, and bring people along with data-driven storytelling.
- Represent the product at every altitude. Communicate strategy, progress, and trade-offs credibly to individual contributors and to CTO-level leadership, in written narratives, demos, and live forums.
- Raise the bar around you. Mentor product managers, coach AI experimentation and prototyping across the team, and evangelize emerging AI capabilities and industry patterns that change what's possible for the product.
Qualifications
What you'll bring:
- Speed as a habit. You drive velocity by identifying the highest-leverage opportunities and moving on them, delivering incrementally under aggressive timelines rather than waiting for a complete picture.
- Thought leadership at the portfolio level. You influence product decisions beyond your own scope, shaping adjacent roadmaps to deliver an optimal, ecosystem-wide end-to-end experience and you set business outcome objectives grounded in a deep understanding of the domain.
- AI-first product judgment. You have shipped AI-powered products and know the difference between a demo and a dependable system. You reason fluently about LLMs, retrieval and grounding, agents and tool use, evaluation, latency, and cost; and you know when a simpler approach is the right answer.
- Data platform depth. You have built data or platform products where schema, entity resolution, lineage, freshness, and governance were the product, and you can hold a rigorous conversation with data engineers and architects about all of it.
- Customer obsession with hard-to-reach customers. You get in the workflow, evangelize customer personas to align teams, and challenge those teams to deliver delightful experiences including when your customers are busy senior leaders who will give you one chance.
- Structured thinking in high ambiguity. You break down complex, cross-domain problems using data, AI methodology, and domain expertise, and you continuously refine your approach as new insight arrives.
- Influence without authority. You are a skilled storyteller, communicator, and negotiator who builds shared understanding across teams and functions, actively seeks feedback, and drives complex strategic decisions to closure.
- Hands-on instincts. You prototype to communicate; using AI tooling, self-serve dashboards, and rapid mockups to set a clear vision and accelerate multiple teams, rather than describing an idea in a document and hoping it lands.
- A passion for continuous learning. You experiment with emerging AI tools and paradigms as a matter of routine, and you teach what you learn.
8+ years in product management designing and delivering world-class platform, data, or large-scale enterprise products; experience with AI/ML-powered products strongly preferred
- Track record of defining and delivering platform capabilities that serve enterprise needs, taking products from MVP to meaningful scale and adoption
- Demonstrable technical depth in SaaS and data-intensive software development, with strong engagement with engineering and architecture teams; delivery of API-first or agent-consumable offerings that developers and AI systems love using
- Hands-on experience shipping products built on large language models; retrieval and grounding strategies, agent or tool-use patterns, conversational and assistant interfaces, including how to evaluate and improve answer quality
- Experience navigating enterprise data governance: access control, tenancy, consent, retention, and auditability
- Proven success taking a data-driven approach to prioritization, experimentation, and measurement, with impactful business results to show for it
- Experience as a product owner in an Agile environment, establishing clear requirements through well-formed epics and data contracts
- Demonstrated ability to define a compelling vision, build cross-organizational coalitions, and inspire teams to deliver new innovations
- Inspiring communication skills, from small working teams to executive audiences
- BS/MS in a technical field preferred, or equivalent work experience
Pay
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 $205,500 - $278,000
We need to ensure no duplicate content. The overview paragraph: we need to copy exactly the overview text from the posting: "Overview The Technology Strategic Programs organization delivers for Intuit and Tech Strategy by transforming and driving how our technology ecosystem operates to accelerate outcomes for our customers. This small, yet mighty team works with senior leaders and partners across the company. We focus on strategic planning, the operating rhythm, executive narratives, workforce programs, and delivering intelligent insights that accelerate execution across the tech portfolio and our highest-priority business growth areas. We are building the execution intelligence layer for Intuit: a governed, enterprise-scale data platform that ingests program and portfolio data from the systems where work actually happens, resolves entities across all of them, and serves one trusted, cited view to any AI agent, assistant, or dashboard that needs it. Today, execution data is fragmented across collaboration, planning, and delivery tools. Every program manager rebuilds the same context from scratch, friction surfaces as escalation rather than early signal, and leaders pay a coordination tax to answer basic questions about how we are tracking against our commitments. Our platform makes that context durable - across sessions, across people, and across whatever AI tool comes next. We are looking for a Senior Staff Product Manager to own this product end to end. You will set the strategy and roadmap, drive prioritization of data onboarding across sources and customer segments, and translate the needs of program managers and senior technology leaders into crisp, buildable requirements for a platform that is equal parts data infrastructure and
Overview
. The instruction says use for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It doesn't forbid other headings, but we should follow similar pattern. We'll include Overview, Responsibilities, Qualifications, Pay.
We need to preserve all substantive information, not invent or summarize away detail. Use - for any list of duties/requirements/benefits. In Responsibilities there is a list of bullet points starting with "Own the product end to end." etc. Those are sentences each starting with a verb. We'll make each as
- . Also Qualifications has bullet points: "What you'll bring:" then a list of traits (Speed as a habit, Thought leadership..., etc). Then after that there is another "Qualifications" heading with more detailed qualifications (8+ years...). We need to combine? The posting repeats "Qualifications" heading twice: first a list of traits, then a second "Qualifications" with more detailed requirements. We should combine them under one Qualifications section, preserving all content. We'll make a
for the traits and another for the detailed requirements? Could be two lists. We'll keep them as separate under same heading.
We need to remove boilerplate: cookie/navigation/legal/EEO/equal-opportunity/scam-warning, duplicated lines, application instructions. There's none besides the compensation paragraph.
We need to output only HTML fragment, no /, no markdown fences.
Let's craft:
Overview
... (the overview paragraph).
Responsibilities
- Own the product end to end. Define and evolve the vision, strategy, and multi-quarter roadmap for the execution data platform and the AI experiences built on it — from data ingestion and entity resolution through agents, skills, briefings, and portfolio-level insights.
- Drive prioritization of data onboarding. Own the sequencing decisions that determine the platform's value: which data sources to integrate next, which programs and organizations to onboard, and what "good enough" data quality looks like at each stage. Make these calls transparently, with a clear framework and a defensible read of customer impact versus cost.
- Translate customer needs into requirements engineers can build from. Spend real time with program managers, goal owners, and technology leaders to understand their jobs-to-be-done. Turn messy, high-stakes workflows into problem statements, requirements, data contracts, and success metrics that hold up under scrutiny.
- Define durable, reusable capabilities. Expand individual business needs into capability strategies — ensuring cross-capability durability and AI reusability, so each new source, entity, or skill compounds rather than fragments. Prevent one-off builds from becoming the product.
- Partner deeply with engineering and architecture. Facilitate technical decisions and trade-offs with an AI-first mindset — retrieval and grounding strategy, entity resolution approach, freshness versus cost, agent orchestration, evaluation and quality bars. Hold a high bar for answers that are accurate, cited, and trustworthy.
- Own trust, privacy, and governance as product surface. Work with security, legal, privacy, and compliance partners to define access controls, tenancy isolation, consent, and auditability — and design the transparency experience that earns adoption incrementally rather than demanding it upfront.
- Set the metrics and manage to them. Define the customer benefit and business outcome metrics for the platform, instrument them, and use data, experimentation, and AI-assisted analysis to drive prioritization and demonstrate impact.
- Build the coalition. Influence and align product, engineering, program management, and business teams that each have their own roadmaps and incentives. Navigate the tension between long-term platform investment and near-term customer commitments, and bring people along with data-driven storytelling.
- Represent the product at every altitude. Communicate strategy, progress, and trade-offs credibly to individual contributors and to CTO-level leadership, in written narratives, demos, and live forums.
- Raise the bar around you. Mentor product managers, coach AI experimentation and prototyping across the team, and evangelize emerging AI capabilities and industry patterns that change what's possible for the product.
Qualifications
What you'll bring:
- Speed as a habit. You drive velocity by identifying the highest-leverage opportunities and moving on them, delivering incrementally under aggressive timelines rather than waiting for a complete picture.
- Thought leadership at the portfolio level. You influence product decisions beyond your own scope, shaping adjacent roadmaps to deliver an optimal, ecosystem-wide end-to-end experience and you set business outcome objectives grounded in a deep understanding of the domain.
- AI-first product judgment. You have shipped AI-powered products and know the difference between a demo and a dependable system. You reason fluently about LLMs, retrieval and grounding, agents and tool use, evaluation, latency, and cost; and you know when a simpler approach is the right answer.
- Data platform depth. You have built data or platform products where schema, entity resolution, lineage, freshness, and governance were the product, and you can hold a rigorous conversation with data engineers and architects about all of it.
- Customer obsession with hard-to-reach customers. You get in the workflow, evangelize customer personas to align teams, and challenge those teams to deliver delightful experiences including when your customers are busy senior leaders who will give you one chance.
- Structured thinking in high ambiguity. You break down complex, cross-domain problems using data, AI methodology, and domain expertise, and you continuously refine your approach as new insight arrives.
- Influence without authority. You are a skilled storyteller, communicator, and negotiator who builds shared understanding across teams and functions, actively seeks feedback, and drives complex strategic decisions to closure.
- Hands-on instincts. You prototype to communicate; using AI tooling, self-serve dashboards, and rapid mockups to set a clear vision and accelerate multiple teams, rather than describing an idea in a document and hoping it lands.
- A passion for continuous learning. You experiment with emerging AI tools and paradigms as a matter of routine, and you teach what you learn.
8+ years in product management designing and delivering world-class platform, data, or large-scale enterprise products; experience with AI/ML-powered products strongly preferred
- Track record of defining and delivering platform capabilities that serve enterprise needs, taking products from MVP to meaningful scale and adoption
- Demonstrable technical depth in SaaS and data-intensive software development, with strong engagement with engineering and architecture teams; delivery of API-first or agent-consumable offerings that developers and AI systems love using
- Hands-on experience shipping products built on large language models; retrieval and grounding strategies, agent or tool-use patterns, conversational and assistant interfaces, including how to evaluate and improve answer quality
- Experience navigating enterprise data governance: access control, tenancy, consent, retention, and auditability
- Proven success taking a data-driven approach to prioritization, experimentation, and measurement, with impactful business results to show for it
- Experience as a product owner in an Agile environment, establishing clear requirements through well-formed epics and data contracts
- Demonstrated ability to define a compelling vision, build cross-organizational coalitions, and inspire teams to deliver new innovations
- Inspiring communication skills, from small working teams to executive audiences
- BS/MS in a technical field preferred, or equivalent work experience
Pay
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 $205,500 - $278,000
We need to ensure no duplicate content. The overview paragraph: we need to copy exactly the overview text from the posting: "Overview The Technology Strategic Programs organization delivers for Intuit and Tech Strategy by transforming and driving how our technology ecosystem operates to accelerate outcomes for our customers. This small, yet mighty team works with senior leaders and partners across the company. We focus on strategic planning, the operating rhythm, executive narratives, workforce programs, and delivering intelligent insights that accelerate execution across the tech portfolio and our highest-priority business growth areas. We are building the execution intelligence layer for Intuit: a governed, enterprise-scale data platform that ingests program and portfolio data from the systems where work actually happens, resolves entities across all of them, and serves one trusted, cited view to any AI agent, assistant, or dashboard that needs it. Today, execution data is fragmented across collaboration, planning, and delivery tools. Every program manager rebuilds the same context from scratch, friction surfaces as escalation rather than early signal, and leaders pay a coordination tax to answer basic questions about how we are tracking against our commitments. Our platform makes that context durable - across sessions, across people, and across whatever AI tool comes next. We are looking for a Senior Staff Product Manager to own this product end to end. You will set the strategy and roadmap, drive prioritization of data onboarding across sources and customer segments, and translate the needs of program managers and senior technology leaders into crisp, buildable requirements for a platform that is equal parts data infrastructure and
- for the traits and another
- Own the product end to end. Define and evolve the vision, strategy, and multi-quarter roadmap for the execution data platform and the AI experiences built on it — from data ingestion and entity resolution through agents, skills, briefings, and portfolio-level insights.
- Drive prioritization of data onboarding. Own the sequencing decisions that determine the platform's value: which data sources to integrate next, which programs and organizations to onboard, and what "good enough" data quality looks like at each stage. Make these calls transparently, with a clear framework and a defensible read of customer impact versus cost.
- Translate customer needs into requirements engineers can build from. Spend real time with program managers, goal owners, and technology leaders to understand their jobs-to-be-done. Turn messy, high-stakes workflows into problem statements, requirements, data contracts, and success metrics that hold up under scrutiny.
- Define durable, reusable capabilities. Expand individual business needs into capability strategies — ensuring cross-capability durability and AI reusability, so each new source, entity, or skill compounds rather than fragments. Prevent one-off builds from becoming the product.
- Partner deeply with engineering and architecture. Facilitate technical decisions and trade-offs with an AI-first mindset — retrieval and grounding strategy, entity resolution approach, freshness versus cost, agent orchestration, evaluation and quality bars. Hold a high bar for answers that are accurate, cited, and trustworthy.
- Own trust, privacy, and governance as product surface. Work with security, legal, privacy, and compliance partners to define access controls, tenancy isolation, consent, and auditability — and design the transparency experience that earns adoption incrementally rather than demanding it upfront.
- Set the metrics and manage to them. Define the customer benefit and business outcome metrics for the platform, instrument them, and use data, experimentation, and AI-assisted analysis to drive prioritization and demonstrate impact.
- Build the coalition. Influence and align product, engineering, program management, and business teams that each have their own roadmaps and incentives. Navigate the tension between long-term platform investment and near-term customer commitments, and bring people along with data-driven storytelling.
- Represent the product at every altitude. Communicate strategy, progress, and trade-offs credibly to individual contributors and to CTO-level leadership, in written narratives, demos, and live forums.
- Raise the bar around you. Mentor product managers, coach AI experimentation and prototyping across the team, and evangelize emerging AI capabilities and industry patterns that change what's possible for the product.
- Speed as a habit. You drive velocity by identifying the highest-leverage opportunities and moving on them, delivering incrementally under aggressive timelines rather than waiting for a complete picture.
- Thought leadership at the portfolio level. You influence product decisions beyond your own scope, shaping adjacent roadmaps to deliver an optimal, ecosystem-wide end-to-end experience and you set business outcome objectives grounded in a deep understanding of the domain.
- AI-first product judgment. You have shipped AI-powered products and know the difference between a demo and a dependable system. You reason fluently about LLMs, retrieval and grounding, agents and tool use, evaluation, latency, and cost; and you know when a simpler approach is the right answer.
- Data platform depth. You have built data or platform products where schema, entity resolution, lineage, freshness, and governance were the product, and you can hold a rigorous conversation with data engineers and architects about all of it.
- Customer obsession with hard-to-reach customers. You get in the workflow, evangelize customer personas to align teams, and challenge those teams to deliver delightful experiences including when your customers are busy senior leaders who will give you one chance.
- Structured thinking in high ambiguity. You break down complex, cross-domain problems using data, AI methodology, and domain expertise, and you continuously refine your approach as new insight arrives.
- Influence without authority. You are a skilled storyteller, communicator, and negotiator who builds shared understanding across teams and functions, actively seeks feedback, and drives complex strategic decisions to closure.
- Hands-on instincts. You prototype to communicate; using AI tooling, self-serve dashboards, and rapid mockups to set a clear vision and accelerate multiple teams, rather than describing an idea in a document and hoping it lands.
- A passion for continuous learning. You experiment with emerging AI tools and paradigms as a matter of routine, and you teach what you learn.
- Track record of defining and delivering platform capabilities that serve enterprise needs, taking products from MVP to meaningful scale and adoption
- Demonstrable technical depth in SaaS and data-intensive software development, with strong engagement with engineering and architecture teams; delivery of API-first or agent-consumable offerings that developers and AI systems love using
- Hands-on experience shipping products built on large language models; retrieval and grounding strategies, agent or tool-use patterns, conversational and assistant interfaces, including how to evaluate and improve answer quality
- Experience navigating enterprise data governance: access control, tenancy, consent, retention, and auditability
- Proven success taking a data-driven approach to prioritization, experimentation, and measurement, with impactful business results to show for it
- Experience as a product owner in an Agile environment, establishing clear requirements through well-formed epics and data contracts
- Demonstrated ability to define a compelling vision, build cross-organizational coalitions, and inspire teams to deliver new innovations
- Inspiring communication skills, from small working teams to executive audiences
- BS/MS in a technical field preferred, or equivalent work experience
- for the detailed requirements? Could be two lists. We'll keep them as separate
- under same heading.
We need to remove boilerplate: cookie/navigation/legal/EEO/equal-opportunity/scam-warning, duplicated lines, application instructions. There's none besides the compensation paragraph.
We need to output only HTML fragment, no /, no markdown fences.
Let's craft:
Overview
... (the overview paragraph).
Responsibilities
Qualifications
What you'll bring:
8+ years in product management designing and delivering world-class platform, data, or large-scale enterprise products; experience with AI/ML-powered products strongly preferred
Pay
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 $205,500 - $278,000
We need to ensure no duplicate content. The overview paragraph: we need to copy exactly the overview text from the posting: "Overview The Technology Strategic Programs organization delivers for Intuit and Tech Strategy by transforming and driving how our technology ecosystem operates to accelerate outcomes for our customers. This small, yet mighty team works with senior leaders and partners across the company. We focus on strategic planning, the operating rhythm, executive narratives, workforce programs, and delivering intelligent insights that accelerate execution across the tech portfolio and our highest-priority business growth areas. We are building the execution intelligence layer for Intuit: a governed, enterprise-scale data platform that ingests program and portfolio data from the systems where work actually happens, resolves entities across all of them, and serves one trusted, cited view to any AI agent, assistant, or dashboard that needs it. Today, execution data is fragmented across collaboration, planning, and delivery tools. Every program manager rebuilds the same context from scratch, friction surfaces as escalation rather than early signal, and leaders pay a coordination tax to answer basic questions about how we are tracking against our commitments. Our platform makes that context durable - across sessions, across people, and across whatever AI tool comes next. We are looking for a Senior Staff Product Manager to own this product end to end. You will set the strategy and roadmap, drive prioritization of data onboarding across sources and customer segments, and translate the needs of program managers and senior technology leaders into crisp, buildable requirements for a platform that is equal parts data infrastructure and