• Engage with several Snowflake customers • Lead technical streams of client data platform implementations and on-boarding efforts • Collaborate with Customer Engagement and Delivery matrix resources for project management and functional support • Help clients troubleshoot implementations and integrate the product • Identify, document, triage, and track issues for resolution • Contribute to the growth of Customer Delivery and Training teams through documentation and optimization • Gather product feedback from customers to inform new features

• Work on the development, maintenance and evolution of backend solutions with a focus on distributed architecture and high availability • Develop and maintain APIs using .NET and GraphQL, ensuring performance, scalability and security • Build and support solutions based on a microservices architecture, promoting decoupling and efficiency between components • Perform integrations between internal financial systems and platforms in a Banking-as-a-Service (BaaS) context • Implement tasks from technical backlog stories, ensuring a complete understanding of requirements before execution • Actively participate in technical refinements, contributing critical analysis, technical feasibility assessments and defining the best implementation approach • Challenge requirements and identify possible gaps, inconsistencies or improvement opportunities before development • Propose technical solutions and architectural evolutions based on backend development and system integration best practices • Collaborate with technical teams on the continuous evolution of product, architecture and internal processes • Take ownership of deliverables, demonstrating technical accountability and a clear focus on business impact.

• Desenvolvimento de software embarcado utilizando linguagem C e C++ • Desenvolvimento de testes automáticos para garantia da qualidade dos produtos • Participação em projetos da área

• Designing systems that allow AI to participate directly in developer workflows • Building infrastructure that connects language models with the editor and developer tools • Developing context systems that help models reason about large codebases • Designing evaluation frameworks for AI-assisted development • Improving the reliability, latency, and cost efficiency of AI features • Working closely with editor and infrastructure engineers to ship ideas quickly • Pair programming with teammates to explore ideas and refine systems together