Director, Product Management - Data Transformation (Frontier Engineering)

US-CA-Menlo Park · US-WA-Bellevue · 12+ years experience · Full-time · Hybrid · Posted Oct 14, 2026

At a glance

Snowflake seeks a Director, Product Management - Data Transformation within the Frontier Engineering organization to own the foundational product portfolio enabling enterprise Agentic AI.
This role involves setting product vision, leading a team of PMs, and delivering tooling to turn fragmented legacy infrastructure into an AI-first enterprise platform.

Define Vision & Strategy: Own the strategic roadmap across the full transformation stack—from platform migration to semantic and context layers—ensuring every initiative moves enterprises closer to an AI-ready Snowflake platform.
Lead & Scale a High-Performing PM Team: Build, mentor, and manage a team of product managers, fostering a culture of high velocity, accountability, and technical rigor.
Partner with Engineering Leadership: Deeply engage with technical leaders to shape execution strategies, balancing engineering effort and long-term architectural scalability across complex pipeline and query transformation workloads.
Drive Execution with an MVP Mindset: Champion speed and clarity, setting precise roadmap milestones, prioritizing ruthlessly, and ensuring seamless cross-functional alignment.
Own GTM & Customer Adoption: Partner with Marketing, field teams, and executive customers to position transformation capabilities, accelerate migrations, and drive platform adoption.
Drive Thought Leadership: Serve as an external voice for Snowflake, helping shape the industry narrative around legacy modernization, automated pipeline refactoring, and AI-first data architecture.

12+ Years in Data Infrastructure: Proven track record delivering enterprise-scale data platforms, migration tooling, query engines, or pipeline infrastructure.
Proven PM Leadership: Strong experience building, managing, and mentoring high-performing product management teams.
Deep Technical Foundation: Hands-on familiarity with modern data ecosystems—including SQL execution, ETL/ELT patterns, declarative pipelines, and workflow orchestration.
Applied GenAI Exposure: Practical understanding of AI-assisted capabilities, such as automated code translation, developer co-pilots, semantic validation, and LLM-driven refactoring.
Strategic & Analytical Execution: Ability to navigate technical ambiguity, make sound decisions using data and instincts, and reduce time-to-value for enterprise customers.
Executive Communication: Exceptional ability to translate complex architectural capabilities into clear, compelling narratives for customers, field leadership, and executives.