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Snowflake is seeking a Manager of Applied Field Engineering to lead a high-performing team of technical specialists focused on AI/ML and advanced analytics. This is a hands-on leadership role where you will manage Applied Field Engineers who specialize in Generative AI, Machine Learning, and Advanced Analytics, driving customer success and consumption activation.
Key responsibilities include:
**Technical Execution & Consumption Activation:** Lead the team to ensure customers successfully move AI/ML workloads into production and realize contracted credit value. Coach AFEs on technical sales best practices, review customer architectures to prevent technical debt, and actively participate in customer engagements as a player-coach, modeling excellence in architectural and executive-level conversations.
**Product Feedback & Cross-Functional Collaboration:** Aggregate field insights and communicate them to senior leadership, surfacing recurring product gaps and customer blockers to inform roadmap decisions. Partner with Sales leadership to align technical resources with regional pipeline and key account priorities. Participate in Communities of Practice and support knowledge-sharing across the team.
**Team Leadership & Development:** Recruit, onboard, and develop a team of Applied Field Engineers with focus on technical growth and performance management. Build a culture of technical sales excellence where AFEs serve as trusted advisors throughout the sales and post-sales lifecycle. Conduct regular 1:1s, provide ongoing feedback, and support career development for direct reports.
Required qualifications: 8+ years in pre-sales, technical sales, or technical consulting; 2+ years of people management experience leading technical or specialist teams; hands-on depth in GenAI/LLMs, Machine Learning, Data Engineering, or Cloud Data Architecture; ability to engage confidently with VP and Director-level stakeholders; university degree in computer science, engineering, mathematics, or related field (or equivalent experience). Experience with consumption-based models and driving actual activation and usage of software services is essential.
