Principal Product Manager, Data & AI Platforms

As a Principal Product Manager, Data & AI Platforms at Smartsheet, you will serve as a visionary leader, responsible for the strategic direction and execution of our foundational data ecosystem. You will treat our data and AI infrastructure as a premier product, ensuring it is of high quality, architected for massive scale, and directly aligned with Smartsheet’s mission-critical business and advanced AI objectives. This is a high-impact role that bridges engineering excellence with measurable business value, driving the democratization of data and the enablement of advanced AI capabilities across the global organization.

You Will:

• Drive Strategic Alignment: Lead the alignment of data governance and AI-driven use cases across Engineering, Product, and Business organizations to ensure unified goals, rigorous prioritization, and clear scoping of platform capabilities.
• Lead Product Lifecycle: Manage both inbound (requirements gathering, strategic roadmap definition) and outbound (internal/external evangelism, community engagement, customer feedback loops) product management for the data and AI platform.
• Own the Execution Roadmap: Define and drive the execution roadmap for critical data platform features, including the enablement of AI capabilities, streamlined data ingestion/onboarding, and foundational data vision initiatives.
• Champion Governance & Quality: Own the business-stakeholder perspective for data quality and governance, ensuring the platform provides reliable, secure, and compliant data assets for all consumers.
• Deliver High-Quality Requirements: Provide a continuous stream of detailed requirements with clear, measurable acceptance criteria, demonstrating a deep understanding of both upstream data source complexities and downstream end-user consumption needs.
• Engage Stakeholders Extensively: Partner with cross-functional executive leaders to inform platform roadmaps and ensure the data strategy remains strategically aligned with Smartsheet’s overall product and business platform goals.
• Act as a Data Ambassador: Represent the data and AI platform's capabilities internally and externally, clearly communicating the value proposition of features related to data retention, security, compliance, reliability, and integrity.
• Provide Mentorship: Offer informal leadership and mentorship to Engineering and Product team members, fostering a culture of technical excellence, accountability, and collaborative delivery.
  
  

Key Responsibilities:

• Data as a Product (DaaP): Evangelize and implement a "Data as a Product" mindset, shifting the organization from reactive data requests to a self-service, scalable, and visionary data platform.
• AI Enablement: Partner closely with AI and Engineering teams to integrate AI-driven use cases into the core data fabric, ensuring the platform robustly supports advanced machine learning, predictive analytics, and process automation.
• Infrastructure Strategy: Leverage a strong, current understanding of Data Lakes, Data Mesh principles, Medallion Architecture, and modern cloud data platforms (e.g., Databricks, Snowflake) to guide the technical evolution of Smartsheet’s data architecture.
• Data Integrity & Semantic Impact: Define and manage the semantic layer and data integrity practices to ensure data is accurate, fresh, and trusted across all business units.
• Compliance & Ethics: Establish and automate robust data governance standards, including metadata management and strict privacy compliance (GDPR, CCPA, SOC2), while maintaining engineering velocity.

Qualifications & Skills:

• Experience: 8+ years of experience in Product Management, with a significant and proven focus on AI & data infrastructure, core backend platforms, or high-scale technical SaaS products.
• Execution Focus: Proven track record of owning and successfully driving execution roadmaps for critical, scalable, and reliable data systems.
• Technical Depth: Extensive and current understanding of:
• AI Foundations and Platforms: Agentic frameworks, transformer architectures, prompt engineering,rag, fine-tuning techniques, vector databases, and embedding strategies.
• Data Platforms: Deep familiarity with leading cloud data platforms (e.g., Databricks, Snowflake).
• Data Warehousing: Knowledge of Dimensional Modeling and Data Vault 2.0 (DV2.0).
• Architecture: Experience with Medallion Architecture and contemporary Data Integrity practices.
• Communication Mastery: Exceptional ability to translate complex technical debt or architectural needs into clear business value, actionable initiatives, and precise "acceptance criteria" for diverse technical and non-technical stakeholders.
• Customer-Centricity: Ability to treat internal developers, analysts, and external partners as "customers," utilizing research and data to identify and remove friction in the data consumption and contribution journey.
• Architectural Fluency: High-level understanding of data governance, semantic impact, and data modeling, with the ability to lead and navigate complex architectural discussions with senior engineering teams.