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Lead a 10-engineer team building developer experience surfaces for data engineering (browser IDE, VS Code-grade desktop, CLI, observability UIs). Drive product roadmaps, cross-functional collaboration, hands-on prototyping, customer engagement, and mentoring while shipping features like dbt Projects, DCM, and Pipeline Builder.

The Senior Product Manager for Data Observability will define product strategy, lead cross-functional teams, and oversee development of AI-powered data monitoring and quality solutions.

Lead acquisition and management of strategic Financial Services partners, create and execute GTM plans, drive partner and customer adoption of Financial Services products, and collaborate cross-functionally with Sales, GTM, Marketing, Operations, and Product to grow Snowflake’s Data Cloud ecosystem.

Lead large, complex Snowflake implementations and cloud data programs as the program lead for Fortune 500 customers. Drive vision, roadmap, risk mitigation, budget governance, data migrations, and executive stakeholder alignment to deliver on-time, on-budget outcomes and measurable customer value.

Build and improve an AI-native virtualization platform that makes applications work on Snowflake. Work spans AI-driven software synthesis, query parsing/optimization, wire protocols, and data ingest/egress. Collaborate with product, solutions, and field engineering to enable customer migrations and define AI-based engineering processes.

Lead design and implementation of scalable distributed systems and cloud automation for sensitive public-sector environments. Deploy and maintain Kubernetes clusters and cloud compute in air-gapped settings, implement IaC and CI/CD pipelines, ensure security/compliance and operational readiness, troubleshoot network and systems issues, and collaborate with product teams and customers.

Lead multiple engineering teams to design and deliver Snowflake's OLTP database platform. Set technical strategy, drive architecture and reliability for transactions, concurrency, storage, replication, and scalability. Recruit and develop leaders, ensure operational readiness, and apply AI-assisted development to improve velocity and quality across design, implementation, and production operations.

Lead pre-sales engagements by presenting Snowflake to execs and technical audiences, delivering demos and proofs-of-concept, designing solutions, collaborating with product and engineering, maintaining competitive knowledge, supporting large deals and channel partners, and traveling (~30%).

Lead and scale a global AI Solutions engineering organization; define technical vision; design, build, and deliver enterprise-grade AI/ML solutions using Snowflake Cortex and Snowflake ML; partner with Fortune 100 customers and GTM teams; recruit and mentor managers; influence product roadmaps and budgets; drive best practices and continuous improvement across AI methodologies.

In this role, you will lead the AI engineering lifecycle for Snowflake Database Engineering products, enhancing workflows and collaborating to address customer needs.