In the context of modern data engineering, the ability to build scalable, maintainable, and low-code ETL (Extract, Transform, Load) workflows is becoming increasingly critical. This thesis presents the design and implementation of a modular ETL framework using Workato, a cloud-based integration and automation platform (iPaaS). The proposed workflow orchestrates data extraction from a PostgreSQL source hosted on Supabase, applies structured transformations and loads the results into multiple target tables, while enforcing business rules and referential integrity. The solution is structured into two pipeline levels (L0 and L1), which reflect the progressive stages of data preparation and validation, and leverages Workato Recipes and Recipe Functions to ensure modularity and reusability.