Summary
Jorge Da Silva is a data engineer with 11 years of multidisciplinary experience blending data infrastructure, ML research and software development, currently building scalable ETL pipelines at National Bank of Canada. He designs cloud-native data platforms using Python, Spark and AWS services (Glue, Lambda, Step Functions, RDS, S3) and has a track record of automating pipelines to cut ETL costs by over 60%. His background spans BI, dimensional modeling and self-service analytics—having implemented a Data Mesh MVP and deployed Metabase and Power BI solutions to democratize insights. Jorge pairs an MSc in Biomedical Engineering (ML focus) and R&D experience in biomedical signal classification with production-grade engineering, making him comfortable moving models from research into robust data flows. Comfortable in Agile teams and infrastructure-as-code (Terraform, Jenkins), he thrives on optimizing processes and enabling data science productivity. An unexpected strength is his electrical engineering roots and field coordination experience, which inform his systems-thinking approach to complex, cross-functional data projects.
10 years of coding experience
8 years of employment as a software developer
Bachelor of Electrical Engineering, Electrical and Electronics Engineering, Bachelor of Electrical Engineering, Electrical and Electronics Engineering at Universidade Católica de Petrópolis
Nanodegree, Data Engineering, Data Modeling, Cloud Warehouses, Spark and Data Lakes, Pipelines with Airflow, Nanodegree, Data Engineering, Data Modeling, Cloud Warehouses, Spark and Data Lakes, Pipelines with Airflow at Udacity
Master’s Degree, Biomedical Engineering, Python, Data Modeling, Electronics, Machine Learning, Statistic, Human Anatomy, Master’s Degree, Biomedical Engineering, Python, Data Modeling, Electronics, Machine Learning, Statistic, Human Anatomy at Universidade de Brasília
Portuguese, French, English, Spanish