Senior Technical Success Manager - São Paulo

Amplitude Amplitude · Data AI · Brazil · Remote · Customer Success : TSM

This role is a Senior Technical Success Manager for an AI analytics platform. The primary responsibility is to own customer deployment, adoption, and value realization for a portfolio of enterprise accounts. This involves managing the customer journey from presales through renewal, ensuring customers achieve measurable business impact from the platform's AI features. The role requires strong technical fluency with the platform's data architecture, instrumentation, and AI agent configurations, as well as business acumen to connect platform usage to customer outcomes.

What you'd actually do

  1. Own customer deployment, adoption, and outcomes for a portfolio of 10-20 accounts, from presales through renewal
  2. Lead deployment planning and implementation across your book, e.g.: scoping, deployment plan creation, kickoff meetings, use case alignment, coordination of delivery (e.g. managing internal and partner teams), project reporting
  3. Track and manage risks and issues across active deployments, driving resolution before they impact timelines or outcomes
  4. Monitor portfolio health through AI-augmented signals, engaging proactively to accelerate adoption, drive renewals, and deepen stakeholder engagement
  5. Drive product adoption by running live working sessions, identifying new use cases to expand value over time, and showcasing new platform capabilities with a point of view on how they enable customer growth

Skills

Required

  • Customer deployment and adoption ownership
  • Technical fluency with data architecture, instrumentation, and AI agent configurations
  • Business acumen to connect platform to customer outcomes
  • Delivery excellence and project management
  • Portfolio health monitoring and proactive engagement
  • Executive business review delivery
  • Partnership with Account Executives for renewals and expansion
  • Channeling customer feedback internally

Nice to have

  • Fluency in customer technical ecosystems
  • Experience with data foundations, taxonomy, instrumentation patterns

What the JD emphasized

  • AI agent configuration
  • AI-augmented signals
  • AI agent workflows