Data Quality Engineering: ETL and Data Warehouse Testing

In an era driven by data, organizations rely on accurate information to make critical decisions and train artificial intelligence models. Data Quality Assurance (QA) engineers ensure that data pipelines remain reliable, clean, and error-free from source to destination. This course provides a comprehensive introduction to testing data warehouses and ETL/ELT pipelines. You will transition from understanding core data architecture to designing, executing, and documenting robust data validation strategies that keep data pipelines running smoothly. What you'll learn: - Understand the foundational architecture of data warehousing, dimensional modeling, and schemas. - Compare modern ETL and ELT patterns, identifying when to apply each in modern cloud-based environments. - Design comprehensive test cases for data pipelines, including validation of Slowly Changing Dimensions (SCDs). - Apply SQL queries to perform data reconciliation, duplicate checks, and null-value validation. - Analyze data quality requirements for training machine learning and Large Language Models (LLMs) to prevent bias and hallucinations. - Practice implementing structured data quality checks using standard industry testing principles. You will start by exploring core data warehousing concepts and foundational architectural patterns. From there, you will progress through practical testing scenarios, learning how to write SQL validation scripts and ensure pipeline integrity across modern environments. This course is designed for aspiring data QA engineers, software testers transitioning to data roles, and data analysts wanting to ensure pipeline integrity. A basic understanding of SQL is helpful, as you will read and write test queries throughout the material. Begin your journey into data quality engineering and learn how to secure the integrity of modern data pipelines today.