Senior Full-Stack Developer with over 15 years of experience in enterprise software development.
Bachelor's degree in Computer Science with a postgraduate in Data Science. I work on developing highly complex systems, focused on financial integrations, payment methods, and API architecture.
Experience with Java, Quarkus, Spring Boot, JSF, React, and TypeScript, as well as REST and SOAP integrations, messaging, and relational databases, participating from analysis and architecture to implementation and support of business-critical solutions.
Development of strategic features for an educational platform, focused on API architecture, corporate integrations, and mission-critical financial systems.
Responsible for the end-to-end design and implementation of payment solutions (PIX, recurring credit card, and bank slips), including tokenization, transaction authorization and capture, interest and penalty calculations, PIX QR Code generation, bank slip registration and cancellation, and dynamic payment method switching. Development of SOAP and REST integrations with payment gateways, ERP, and corporate systems, including Cielo, Banco do Brasil, and Benner.
Development of integrations and features using messaging (JMS), WebSockets, and asynchronous processing, as well as architectural improvements focused on performance, memory consumption reduction, observability, security, and architectural standards enforcement through automated tests (ArchUnit).
Development of enterprise applications using Java EE, JSF 2, JPA/Hibernate, PrimeFaces, and PostgreSQL.
Architecture definition, technology stack selection, and full development of Interagili, a system for interaction management, sales support, and customer relationship.
Design and development of a data import system for the Social Assistance Information System (SIAS).
Design and development of a firewall and network infrastructure management solution, including NAT rule administration, access control, and centralized configuration management.
Web application development using PHP (MVC) and PostgreSQL.
Creation of multimedia applications using Blender and Adobe Flash for a distance learning (e-learning) platform.
Term Paper - Georeferenced Recommendation System for Event Suggestion - 2017
Term Paper - Integrated Business Management System - 2011
1st place in the SENAI/SC - CTAI Analytics Hackathon, in partnership with the company AMBEV, which took place on August 2nd and 3rd, 2019.
The case was the “ideal assortment for points of sale” and the target audience were the company's salespeople and their service at points of sale (POS) that were already company customers. The objectives were to increase the sales volume of products (SKUs) that were already purchased by the POS and explore the opportunity to offer other SKUs to complement and maximize the brand's mix at the POS.
The solution implemented was a web application in Flask, where for each POS the system generates a suggested product mix with estimated sales volume based on similar POSs. For this, an exploratory analysis of about 5 million data from the database provided by Ambev was carried out, data processing and use of machine learning models, including KNN to perform the search for similarity between POSs and Random Forest to estimate the volume of sales for the new POS.
2nd place in the SENAI/SC - CTAI BIG DATA Hackathon, in partnership with the company Confederação Nacional das Indústrias - CNI, which took place on November 8th and 9th, 2019.
The case presented by CNI was the “identification of the profile of employees on leave from the industry” and the target audience was SESI employees who identify problems, investigate the causes and offer solutions for partner industries. The challenge, then, was to identify the causes of absence, prevent and reduce absenteeism for health reasons of employees of Brazilian industries.
The solution implemented was an online platform for consultations and analysis of social indicators that allow the presentation of diagnoses of the impact of an employee's profile on his or her absence due to health reasons. For this, RAIS data from the years 2008 to 2011 were used, approximately 256 million data. These were loaded into 4 Stages in the hive through PySpark and partitioned into 200 blocks to enable data manipulation. Dimension tables of informative variables were also worked on, with their descriptions. Based on these data, the Naive Bayes machine learning model was used to estimate the probability of absence through the profile of the employee.