Ambev

• Manage and optimize cloud and technology tool costs, with a focus on AWS • Manage and renew technology contracts, identifying risks, inefficiencies and optimization opportunities • Create and maintain dashboards and reports for costs, efficiency and unit economics • Identify, propose and track savings opportunities (rightsizing, Savings Plans, reservations/Reserved Instances, committed use, etc.) • Partner with SRE and Engineering to execute technical cost-optimization actions • Manage and optimize costs of SaaS tools and platforms • Support strategic decisions by presenting technical and financial trade-offs • Develop FinOps processes: forecasting, budgeting, showback/chargeback and accountability • Promote a culture of cost ownership within technical teams

• Design and lead the evolution of the internal developer platform (Internal Developer Platform), ensuring scalability, governance and an excellent developer experience • Define and implement robust cloud-native architectures on AWS (EKS, ECS, Lambda), making high-impact technical decisions • Serve as a technical reference for full-stack development of the platform (Node.js, Python, React), balancing quality, performance and maintainability • Lead the adoption of Infrastructure as Code (IaC) as an organizational standard, using Terraform, AWS CDK or CloudFormation • Define and evolve golden paths, patterns and frameworks that increase productivity and reduce complexity for engineering teams • Drive automation and self-service infrastructure initiatives at scale • Influence and continuously improve the Developer Experience (DX) across the organization • Work closely with multiple teams to drive platform adoption and best practices • Lead troubleshooting of complex issues and define resilience and observability strategies • Mentor engineers and contribute to the technical growth of the team

• Support the operation and maintenance of AWS infrastructure, ensuring application and database availability • Assist in building and maintaining environments using AWS Lambda, containers, and Kubernetes • Contribute to the reliability and performance of the platform's services • Support the implementation of observability practices (metrics, logs, and traces) • Assist in using tools such as CloudWatch, Grafana, Prometheus, and Datadog • Develop scripts and small automations to reduce manual tasks (toil) • Support the implementation of Infrastructure as Code (IaC) using Terraform • Assist with troubleshooting infrastructure, application, and network incidents • Follow deployment, runtime, and operational standards defined by the team • Participate in incident response, postmortems, and continuous improvement initiatives

• Data modeling (facts, dimensions, metrics) and maintenance of pipelines/transformations using SQL and the team’s tools (e.g., dbt, Spark, or Databricks). • Contribute to quality tests and the documentation of metrics and business rules. • Assist in delivering tables/views for analytical consumption, ensuring best practices (naming, descriptions, consistency). • Work alongside Data Engineering, BI/Analysts, and Data Science (on well-defined tasks). • Participate in governance initiatives (lineage, catalog, access) and in gradual improvements to data reliability.

• Work on the development and evolution of backend solutions, from technical design through implementation and maintenance • Participate in technical refinements, architecture discussions, and solution definition • Collaborate with Engineering, Product, and Design teams • Contribute to technical decisions, standards, and development best practices • Identify, prioritize, and resolve bugs and technical debt • Ensure the quality, stability, and scalability of solutions • Share knowledge and contribute to the Backend Chapter • Support the technical development of other team members (especially at Mid and Senior levels) • Participate in hiring processes, supporting technical interviews (for more senior levels)

• Desenvolver e evoluir modelos de predição para o Zé Delivery. • Criar e manter pipelines de dados e features em PySpark para suportar modelos em escala. • Desenvolver estratégias de recomendação de produtos e ofertas ao longo da jornada do consumidor. • Definir, executar e analisar testes A/B para avaliação de impacto de modelos e campanhas.

• Someone who takes personal responsibility for the squad's results and does not accept 'that's not my problem'. • Technical Proficiency (Hard Skills): • Strong experience with Python/PySpark. • Experience with cloud platforms (AWS or Azure). • Proficiency in recommendation algorithms (Collaborative Filtering, Content-based, Reinforcement Learning for personalization). • Ability to work with real-time models and large volumes of data. • Model validation metrics. • Knowledge of machine learning, statistics, and predictive modeling. • Business understanding, with a focus on impact and data-driven decision making. • Simplicity and Scalability: Focus on building solutions that are simple to maintain and easy to scale across the company. • Long-Term Vision: Someone who will not take ethical or technical shortcuts that create unmanageable debt in the future. • Nice to Have: • Experience with large-scale experimentation and A/B testing. • Cross-functional collaboration: Prior experience partnering with Machine Learning Engineers (MLE) to deploy models. • Familiarity with Multi-armed Bandits. • Data Storytelling: Ability to translate complex technical metrics into business insights for Brand and Product stakeholders. • Cost Awareness: Mindset for optimizing cloud processing costs (managing resources with discipline).