I have worked in the software market for over 20 years, In addition to academic education in renowned schools in Brazil. With perennial discipline, I evolved into a Solution Architect in 2007, modernizing the company's core system. In the early 2010s, I was already leading projects as a project manager. I gained knowledge as a project manager and PMO, I lived with the change from large projects to agile projects and, later, to products.

The technical knowledge, in agile projects and products, made me possible to add more value to the business by technically leading the squads with good TDD and DDD processes in addition to cutting-edge technology for creating frontend, backend, and data engineering. With the market change, the hands-on potential was revalidated, and I kept transferring the knowledge of creating solutions for the business to squads so they can build robust and scalable solutions. Thus, transitioning back to TechLead / solution architect.

Due to the constant business innovation, I am seeking to update myself on the cutting-edge technologies and their tools such as: Java, NodeJS and Python in on-premises environments and in the main clouds AWS, Azure and GCP. With a holistic vision, I lead the team to build microservices with an onion/hexagonal foundation using SOLID Methodology. I am familiar with most of the relational. In NoSQL I have experience with MongoDB, DinamoDB and CosmosDB, Redis cache database and other types of persistence tools. I have constant knowledge in containerization components, code repository, DevOps and CI/CD, always seeking to be up to date. Frontend I usually opt for Angular or React, depending on the business needs, the frontend rendered on the server is the best option. I know the main messaging tools on the market, including MQTT (Mosquitto, RabbitMQ, ActiveMQ), and Kafka.

In search of being up-to-date with the market, I am studying the innovations of data engineering and science, structuring in how I should be better feature engineer. It follows the trends of LMMOPS and MLOPS for the continuous creation and updating models of natural language, regression, image recognition, among others that can leverage the business. Empowering feature engineering with PySpark to create the basic layer of the Data Lake.