As an Analytics Engineer at Nubank I am entrusted with the back engineering of data products, using structures such as data warehouses, data lakes and databases, aiming to improve data work performance and reduce costs. Daily tasks involve performing data modelling, data preparation and/or cleansing, creating and maintaining ETL pipelines, using tools such as Scala, Spark/Databricks, SQL (on BigQuery) and Git on top of CICD pipelines.

My primary responsibilities at IBM involve creating and maintaining data pipelines and ETL processes, which feed machine learning algorithms and other data-related needs. I also develop integrations and migrate data from on-premises servers to cloud environments, ensuring that data is readily available for data scientists, analysts, and business users. To accomplish this, I collaborate closely with various stakeholders, including business and end-users, technical teams, and management, to gather requirements, discuss data architecture details, and implement solutions using tools such as Python, PySpark, SQL Server, Data Factory, Databricks, Data Lake, Azure Machine Learning, Synapse, Snowflake, Linux, and more.In addition to these tasks, I contribute to data classification efforts, employing Machine Learning techniques like Support Vector Machines and Natural Language Processing libraries like Spacy to optimize data preparation.Furthermore, I serve as a technical advisor for several projects, mentor IBMers transitioning to the data field, and actively participate in Generative AI initiatives, both in prototyping and knowledge dissemination phases. I have also implemented solutions using Python, ChromaDB, and Large Language Models in various Generative AI projects.

At Raro Labs I created ETL processes, data pipelines, databases and dashboards, using tools such as Python, PySpark, SQL, AWS services and more.

Performed data analysis/science activities for Banco BMG, using Python, SQL and apache Spark, including:- Natural Language Processing- Business insights extraction- Statistical analysis and other ad hoc studies- Dashboards building- Creation of ETL (data Extraction, Transformation and Loading) processes- Creation of database schemas and migration of SQL data

In Repassa I did data analysis jobs using statistical analysis, Python, its scientific libraries and data visualization libraries. I also created SQL queries, performed recommendation and hypothesis studies. Creating ETL processes using Azure Databricks and integrating a variety of data sources were also part of my daily job.

Data scraping, data cleansing, statistical analysis and forecasting for clients on the advertising line of business.