• Projetar e implementar arquiteturas de segurança Zero Trust em ambientes híbridos e multicloud (GCP, Azure, AWS), incluindo Kubernetes. • Definir e alinhar estratégias de segurança com objetivos de negócios e tecnologia. • Arquitetar e implementar controles de segurança em nuvem cobrindo identidade, rede, carga de trabalho e proteção de dados. • Identificar, avaliar e comunicar riscos de segurança e ameaças emergentes. • Liderar arquitetura de segurança para projetos de TI e nuvem, equilibrando necessidades de negócios, segurança e conformidade. • Colaborar com equipes de DevOps e Cloud para incorporar segurança em pipelines CI/CD e implementações nativas em nuvem. • Supervisionar avaliações de vulnerabilidades, análises de risco e revisões de segurança. • Garantir conformidade com padrões e frameworks de segurança relevantes.

• Consulting, design and implementation of private cloud platforms • Consulting, design and implementation of operating business applications using container technologies • Collaboration with other specialist teams to provide the required infrastructure • Measuring, analyzing and optimizing performance and stability • Building and operating distributed systems using the DevOps approach • Promoting continuous integration & deployment processes • Troubleshooting and resolving issues in container environments

• Design and evolve the enterprise Azure Lakehouse architecture, ensuring scalability, performance, and long-term sustainability. • Lead the transformation of classic Data Warehouse environments into modern Lakehouse and Delta Lake-based architectures. • Take architectural ownership from concept through implementation, production rollout, and continuous optimization. • Define and implement architecture principles, standards, patterns, and best practices for data engineering and analytics platforms. • Design and oversee data ingestion, processing, and streaming pipelines using Azure Databricks, Azure Data Factory, Event Hub, and Azure Logic Apps. • Build and optimize data processing solutions using Delta Lake, Spark, Spark Structured Streaming, SQL, and Python. • Define data modeling strategies across data lake and warehouse layers, applying Kimball, Inmon, and Data Vault methodologies. • Collaborate with engineering teams to implement reference architectures, prototypes, and proof-of-concepts. • Enable and coach data engineering teams toward autonomous, high-quality solution development. • Ensure platform designs comply with enterprise security, data protection, and regulatory requirements. • Support advanced analytics, self-service BI, and reporting use cases across the organization. • Work closely with product owners, architects, governance, and business stakeholders in agile delivery environments. • Contribute to continuous improvement of platform architecture, development processes, and operating models.

• Key member of the engineering team, working with client engineering teams, product owners, solutions architects, back-end/front-end developers to drive successful project delivery for our clients and solutions. • Perform client and project discovery activities to understand business and technical requirements, success metrics, and solution architecture. • Act as a trusted advisor to clients, understanding their business challenges and translating them into actionable, high-impact AI/ML solutions. • Develop clear, compelling AI/ML roadmaps and strategies that align with client goals and maximize the use of Google Cloud's AI and data capabilities. • Design and implement end-to-end AI/ML solutions on Google Cloud Platform, including data ingestion, feature engineering, model training, deployment, and monitoring. • Work closely with cross functional team members to drive successful delivery of client projects as well as establish repeatable processes and best practices in a professional services organization.