Alejandro Hernan Colmenarejo

Data Engineering & Quality: Developed and optimized ETL pipelines using PySpark and Python within AWS Glue to accurately and scalably process transactional charging session data.

Data Lake & Medallion Architecture: Architected and developed scalable ETL pipelines using AWS Glue (Python, PySpark), establishing a robust Medallion data architecture (Bronze, Silver, Gold) in Amazon S3 to guarantee data quality and traceability.

CI/CD

ETL using AWS services with Github repository. The purpose of the project is a pipeline which uses Glue Job in Pyspark and Python Scripts stored in Github repository. The pipeline is scalable , just needs a new configuration file with the new implementation and this is stored in AppConfig. Every data is getting from S3 and it is stored in S3 too using layers as bronze, silver and gold. In each layer the data has its own properties and transformations. Services used: AppConfig, Step Function, AWS Glue, Athena.

Cross-Cloud ETL & AI Integration: Designed and executed a cross-cloud strategy to migrate data from AWS S3 to Google Cloud, utilizing Vertex AI for data normalization before loading into BigQuery.