• Collaborate on a comprehensive on-premise data project utilizing open-source technologies such as MinIO, Apache Airflow, Apache Spark, and ClickHouse.• Contribute to the implementation and maintenance of a robust Delta Lake, utilizing Upsert transactions with Merge to increment new data.• Translate stakeholder business rules into effective data processing workflows through the application of Apache Spark (Pyspark), Python, and SQL. This process enables the creation of essential Key Performance Indicators (KPIs) for key users.• Leverage Apache Airflow to monitor the data pipeline, implementing email alerts for proactive issue detection and resolution.• Utilize Apache Airflow and Python to automate web scraping for targeted information. Implement dynamic logics to determine the data collection strategy from the API based on the responses obtained from web scraping.• Extract data from diverse databases, such as MySQL, PostgreSQL, and SQL Server. Apply logic to initially ingest large historical tables, followed by the incorporation of daily incremental data updates.• Utilize GitLab for effective version control and employ CI/CD practices for efficient code build and deployment to homolog and production environments.• Spearheaded the initial setup of a core project on Google Cloud Platform (GCP), incorporating Google Cloud Storage, Composer, Dataproc, and BigQuery to establish a robust Data Lake medallion architecture.

• Implemented end-to-end Data Pipelines by leveraging AWS resources for effective Data Lake management. Utilized Apache Airflow to orchestrate the ETL (Extract, Transform, Load) process, ensuring seamless and automated data workflows.• Executed data ingestion from diverse sources, encompassing databases and APIs such as Google Analytics, Mixpanel, and ActiveCampaign. Employed AWS Glue to crawl a MySQL database on AWS RDS, facilitating the seamless movement of data to AWS S3 for subsequent processing.• Applied advanced data transformation and aggregation techniques to convert raw data into meaningful insights. Leveraged Apache Spark (Pyspark), Python and SQL on AWS EMR (Elastic MapReduce) to handle large-scale data processing, ensuring accuracy and efficiency in data manipulation.• Loaded processed data into ClickHouse, serving as a Data Warehouse for real-time analytical data reports. Utilized SQL queries to extract valuable insights from ClickHouse, enabling timely and informed decision-making.• Implemented Metabase as a Business Intelligence (BI) tool to create dynamic data reports. Integrated charts and dashboards within Metabase, providing stakeholders with a user-friendly interface to explore and interpret data trends.• Orchestrated the setup of Airflow, ClickHouse, and Metabase within Docker containers deployed on AWS EC2 instances. This containerized infrastructure streamlined deployment, maintenance, and scalability, enhancing overall system efficiency.• Implemented GitLab for efficient version control and streamlined deployment, ensuring seamless code transitions from development to production on AWS EC2 instances.

• Developed and implemented a robust ETL pipeline using AWS and Databricks, effectively extracting, transforming, and loading diverse data sources.• Managed data stored in various formats, including CSV and Parquet, within Amazon S3, ensuring seamless accessibility and organization of information.• Conducted comprehensive data engineering tasks, employing Python, SQL, and Spark to clean and transform data, enhancing its quality and usability for analytical purposes.• Leveraged Databricks Jobs Compute functionality to establish a robust framework for orchestrating and executing data pipelines.• Implemented external tables in the Databricks Unity Catalog and used Databricks SQL Serverless for integrating data with Power BI.• Orchestrated the setup and optimization of data in Amazon Redshift Data Warehousing, contributing to efficient storage and retrieval of processed data.• Utilized Power BI to create insightful dashboards and reports, empowering stakeholders with visually appealing and interactive tools for data-driven decision-making.

Period dedicated to improving my English language capabilities as a part-time student, in conjunction with experience working for Limbo Limerick Blow Moulding Limited as a general operator. Experience which developed a better understanding in the plastic industry and injection moulding process. Leveraging skills in time management, teamwork, communication and problem-solving.

• Analyzed customers' needs in order to provide appropriate solutions as well as created budgets and technical proposals regarding CNC machinery (Router, Machining Centre and Laser).• Utilized Excel to store, track and analyze the sales data. Worked closely with the company to identify customer needs and demands. It led to product stock optimization and increased the sales since some customers needed shorter lead time.• Demonstrated the functionality and performance of CNC machinery to customers through tests using simulations on software CAM.

Role: Cost Analyst• Created budgets datasheets for standard and special projects using Excel as well as updated their costs via TOTVS ERP system.• Managed third party suppliers to budget materials, services and accessories.Role: Mechanical Designer• Designed the products ball screws and linear guides through AutoCAD software according to catalog, standards and customers' requirements such as, tolerances, sizes, and respective applications.• Managed the bill of materials used in the designs through the TOTVS ERP system.Accomplishments:• Reduced the budgets elaboration time for standard projects by creating an automate tool with Excel.• Developed a project about the costs and main differences such as components, manufacture and assemble between specific products as a curricular internship. The project was presented to the Centro Universitário - Católica de Santa Catarina.

• Operated a manual lathe machine to manufacture parts according to technical drawing.• Controlled the components stock via SAP ERP system to send to the assembly sector.