In the time of data-driven decision-making, the data validation of ETL (Extract, Transform, Load) pipelines is crucial for delivering high quality information. This study explores the design and testing of an ETL data pipeline built with Apache Airflow, Python Pandas, and Pytest. Airflow orchestrates pipeline workflows, ensuring transformation dependencies and scheduling are managed correctly. Pandas handles data manipulation, offering robust tools for efficient transformations. Pytest enables a structured framework for data attributes testing like reliability, accuracy, consistency, completeness and uniqueness. Overall, the presented approach demonstrates how integrating workflow orchestration, data transformation, and automated testing creates a reliable foundation for trustworthy analytics. By embedding validation directly into the ETL process, potential data quality issues can be detected early, reducing downstream risks and improving decision-making confidence. The combination of Airflow, Pandas, and Pytest provides a scalable and maintainable framework that supports continuous monitoring of data quality, highlighting the importance of test-driven practices in modern data engineering pipelines.