A data quality analyst is responsible for ensuring the accuracy, completeness, and reliability of an organization's data. They perform tasks such as data profiling, cleaning, validation, integration, and monitoring to maintain high-quality data. Key skills required for this role include technical skills (SQL, ETL tools, programming languages), analytical skills, and strong communication skills. Data quality analysts use various tools and technologies like ETL tools, data quality tools, and data visualization tools to manage and analyze data effectively. They also carry out processes such as data standardization, deduplication, enrichment, transformation, and governance to maintain high data standards. Common challenges faced by data quality analysts include data silos, varying data formats, changing data requirements, volume of data, identifying root causes, and opposition to change. To overcome these challenges, organizations should adopt best practices such as defining clear data standards, implementing data governance, automating processes, conducting regular data audits, and fostering a culture of data-driven decision-making.