Data architect/engineer with experience in data science and analytics; also, a Python and DataScience instructor with previous experience in pre-college prep courses;
Developed an AI-powered framework that increased engineering productivity by 8x. Engineers who previously delivered one ingestion pipeline (from source to Silver layer) within a given period can now deliver up to eight pipelines in the same timeframe
Allocated to an American startup, architecting their Data Lake from scratch on AWS.
Migration of Data Factory pipelines with processing via Databricks to DAGs in Airflow on GCP (Cloud Composer) in a large Brazilian retailer.
As a data engineer, I led an engineering team in building a DataLake on AWS stack, with pipelines sourcing from various data origins, such as CDC in relational and non-relational databases, APIs, and Google Forms. These pipelines fueled all data consumption within the company;
I rectified a Lead Scoring pipeline of an XGBoost model by identifying a flaw in the model training. The correction was significant for the company's sales team, which relied on the lead ranking for contact prioritization;
With a focus on data analysis, I rectified the company's credit decision engine by analyzing and cross-referencing data from relational and non-relational databases, resulting in improved fraud containment and communication between the business and development teams.