Wallas Abreu

● Led a full feasibility assessment for Snowflake adoption, conducting kickoff sessions, hands-on evaluations, and delivering a complete POC. Consolidated technical pros/cons and platform trade-offs into clear executive-ready insights. Influenced strategic decisions on the team’s future cloud data architecture. ● Achieved a 40.7% cost reduction in AWS EMR Spark workloads (from US$979.78/month to US$580.61/month) by selecting more cost-efficient EC2 instance types without compromising cluster performance, throughput, or processing capacity. ● Structured 20 Salesforce tables in the data lake company using Spark jobs on EMR, from the landing layer (medallion layer) to the semantic layer, using Dbt and understanding and transforming complex structure queries to enrich 3 main important business views for the company (quotation, proposal and issuance). ● I configured a cron job on the Dremio EC2 instance using an SSH tunnel for access, cleaning up Dremio metadata and generating daily logs to an AWS S3 bucket containing storage capacity, processing capacity, and free storage space on the instance. ● Improved Dremio engine performance through a targeted scale-up strategy, increasing processing power and query speed without additional cost. Resolved overload and inefficient lazy-query issues, resulting in faster report updates and a significantly enhanced user experience. ● Implemented ELT pipelines using Python and Airflow (Kubernetes executor) to migrate data from SQL Server, PostgreSQL, and MongoDB into AWS S3. Orchestrated Spark jobs to process and transform datasets, delivering optimized tables to Dremio for analytics consumption.

● Built data pipelines using Python orchestrated with Apache Airflow. This process accesses the bureau's FTP files on FileZilla, reads the files, and writes the data to the PostgreSQL database, providing excellent governance, auditability, and flexibility for all the company's teams. ● Implemented a robust Python script orchestrated with Apache Airflow to migrate data between SQL Server and PostgreSQL, applying best practices in data pipelines with logs and strategic schedulers to reduce unnecessary process costs, which improved the performance of data analytics queries by 50%. ● Develop intelligent web scraping applications with Python (Selenium) that send an email report to managers with a Power BI screenshot containing customer anti-fraud data and status of the micro services, bringing the latest status to the company's external management area, without the cost of a Power BI web application.

● Develop and maintain a robust data pipeline using Airflow and PySpark, executing jobs that import data from Kafka topics to the data lake on GCP, working with the medallion layer (bronze, silver, and gold). These features provide data domain and management related to sales for the Hering Store. ● Implemented scheduled strategic APIs with AWS Lambda, deployed via a Python script to collect data from Dynatrace, Sensedia, ServiceNow, and Wings. This feature provided governance, control, and reliability data, as well as reducing process costs and direct dependencies on these tools or third parties. ● Solved a complex problem using SQL, transforming and validating data in a semi-structured data provided by APIs, organizing and storing it in a separate layer for better maintenance and usability, delivering quality and usability for the security team.

● Developed two management dashboards for the reallocation and scheduling of healthcare professionals in the Mater Dei hospital (Belo Horizonte) network, with control panels using an open-source tool (Metabase) with a Postgres SQL database, providing a success story for the company in the healthcare sector, increasing the quality and efficiency of service. ● Developed a control panel in Power BI connected to an SQL Server database to provide visibility into sales and renewable energy consumption of the Safira Energia network, through manageable analytical data generation.

● Reduced the time and cost of data migration between databases such as SQL Server, Postgres, and MySQL by around 70% by developing a C# application. This guaranteed data efficiency and quality, included evaluations and validations before and after the process, and delivered improved performance. ● Developed and implemented customized triggers and procedures on clients' MySQL databases to create and assess a calculation rule for each school, ensuring full accuracy and assertiveness, and addressing your needs with automation and intelligence. ● Developed AWS Lambda functions for commercial email events (sent, delivery, opened, bounce) using Node.js and AWS SNS and SES, integrated with the MySQL database, implementing data control and governance across all email campaign information, generating insights and making decisions for all commercial campaigns. ● Created a strategic and efficient dashboard to manage Azure DevOps activities using Power BI. This dashboard contained indicators such as: number of tasks created, number of tasks resolved, number of tasks resolved per analyst, incident type, request type, and others, centralizing all company activities in Azure DevOps.

Issuing invoices, archiving documents (notes, DARFS, payrolls), sending and receiving documents, organizing them and the work environment.