In data engineering, data quality encompasses various aspects, including accuracy, completeness, consistency, timeliness, and reliability of data. Ensuring high data quality involves implementing processes and tools to detect and correct errors, validate data integrity, and enforce standards. This includes data cleaning, data validation, and data profiling techniques that help identify and address data issues before they impact downstream applications or analytics. High data quality is critical for making informed decisions, achieving reliable analytics, and maintaining trust in data-driven processes. Effective data quality management also involves monitoring data quality metrics and continuously improving data processes to adapt to changing business needs and data sources.