• Manage and grow a small, senior team by coaching on live deals, developing judgment and technical depth, and hiring people who make the team better - not just bigger. • Set and uphold a high standard for enterprise discovery, technical evaluations, and successful rollouts that directly impact new customer acquisition and expansion. • Partner closely with Sales as a credible technical counterpart on complex enterprise opportunities, helping shape deal strategy, architecture, and execution quality. • Collaborate across Tailscale to improve how we run proofs of concept, explain technical value, and turn real customer feedback into product direction. • Provide clear, actionable feedback to Product and Engineering grounded in concrete customer use cases and enterprise constraints (not hypotheticals). • Stay current on cloud, DevOps, networking, and security trends, and help the team continue sharpening both technical instincts and communication skills.

• Definição e evolução da arquitetura alvo em Java e AWS (microsserviços, APIs, Kafka); • Configuração do ArchType e padrões técnicos que guiarão o desenvolvimento; • Mapeamento de integrações legadas e redesenho usando APIs, mensageria e conectores; • Aplicação do Strangler Fig Pattern para migração incremental sem interrupção do negócio; • Garantia de escalabilidade, rastreabilidade e equivalência funcional entre legado e moderno.

• Hire, coach, and develop a high-performing team of Solution Engineers • Build both technical depth and consultative selling skills within the team • Foster a culture of curiosity, accountability, and continuous improvement • Establish clear performance expectations and career development paths • Partner with Sales to qualify opportunities and improve deal quality • Identify and communicate product-market fit gaps to the Product team, including timelines for future functionality or confirmation that gaps will not be addressed. • Support successful client transitions from pre-sales to implementation by establishing clear expectations, scope, and handoff processes when product-market fit is confirmed, and clients select FMX. • Ensure delivery of high-quality, tailored product demonstrations that clearly articulate value and differentiation • Validate that proposed solutions align with customer requirements, constraints, and desired outcomes. Identify risks, gaps, or misalignments early in the sales cycle • Oversee technical discovery, solution design, and validation to align with customer needs and use cases • Establish best practices for demos, proofs-of-concept, and technical presentations • Partner with RevOps and/or finance to determine the most effective use of solution engineering resources based on their impact on win rates, average deal size, deal velocity, and SaaS Magic Number (ARR for the quarter divided by sales and marketing spend for the previous quarter) • Reallocate resources and propose changes to the team, their focus, and processes to deliver improvements in win rates, average deal size, deal velocity, and SaaS Magic Number, where an SE resource(s) was utilized to the CRO via business cases • Develop and refine playbooks for discovery, demos, and solution design • Implement tools and processes to improve efficiency and consistency across the team • Drive the use of reusable assets, demo environments, and solution templates • Determine whether specific segments and bands require SE resources or whether AEs can deliver the demo using live software or demoware

• Design and implement data and application integrations using Boomi, APIs, and event-driven patterns • Build and manage secure, scalable integrations across enterprise platforms including ERP, CRM, operational systems, and data platforms • Monitor, troubleshoot, and optimize integration workflows for reliability and performance • Extract, transform, and model structured and semi-structured data for analytics and downstream consumption • Implement and manage data storage solutions including data lakes, data warehouses, and curated data marts • Partner with architects, data scientists, and business teams to design solution patterns that support AI and automation use cases • Enable AI solutions by engineering clean, well-integrated, and governed data inputs • Build and manage automated data and integration pipelines delivering consistent outcomes • Contribute to technical documentation, operational runbooks, and knowledge sharing