Senior Product Manager - Data Observability

Responsibilities

• Own the AI-First Product Vision and Strategy: Define how AI transforms Data Observability at Snowflake — from manual threshold configuration to autonomous anomaly detection, from reactive debugging to agentic root cause analysis, and from static lineage graphs to intelligent pipeline health maps. Architect and own the end-to-end product lifecycle from discovery through launch and adoption.
• Build AI-Powered Observability Capabilities: Drive product development for AI and agentic features across anomaly detection, lineage-powered root cause analysis, and intelligent data quality monitoring. Partner with Snowflake Cortex AI and Snowflake Intelligence teams to integrate AI capabilities natively into the observability surface.
• Lead from the Front: Serve as the cross-functional leader for a dedicated pod of engineers and designers. Drive execution, partner with design, marketing, and go-to-market to bring capabilities to market, and act as the internal and external evangelist for your product area.
• Be the Voice of the Customer: Engage deeply with enterprise data engineers, data platform leads, and executive stakeholders to gather insights, validate hypotheses, and ensure the solutions you build solve their most pressing pipeline reliability and data quality problems.
• Shape Competitive Strategy: Develop a sharp point of view on how Snowflake wins against best-in-class data observability and catalog competitors — and translate that view into a differentiated roadmap that compounds Snowflake's native platform and AI advantage.
• Drive Adoption at Scale: Collaborate with customer success and go-to-market teams to convert shipped capabilities into measurable adoption. Own outcomes, not just output.

Requirements

• A Bachelor's degree in Computer Science, Engineering, or a related technical field; MBA or advanced degree is a plus.
• 5+ years of product management experience, with a proven track record of shipping successful enterprise data infrastructure, data quality, pipeline monitoring, or observability products.
• Deep familiarity with AI/ML concepts and agentic workflows; hands-on experience building or shipping AI-powered or AI-augmented data products is essential.
• Strong understanding of data quality and observability concepts — freshness, volume anomalies, schema drift, lineage, and root cause analysis — and how AI is reshaping each of these problem spaces.
• Demonstrated ability to develop product strategy and translate it into a concrete, actionable roadmap with measurable outcomes.
• Strong customer empathy and experience engaging directly with enterprise data leaders to understand their most pressing pipeline reliability and governance challenges.
• Exceptional communication skills with the ability to align cross-functional teams, influence engineering direction, and present roadmap strategy to executive leadership.
• Fluency using AI tools as a core part of daily product work — not as a productivity add-on, but as an essential operating mode.

Nice-to-Haves

• Prior experience at or deep knowledge of leading data observability and data catalog platforms.
• Familiarity with lineage standards and frameworks (OpenLineage, dbt lineage, Apache Atlas).
• Experience operating in a high-growth, competitive market where you displaced an incumbent.
• Exposure to agentic or AI-powered workflows for automated root cause analysis, intelligent alerting, or data remediation.