I have extensive experience in data cleansing, having worked on projects where I developed and implemented scripts to identify and correct errors in datasets. I utilize SQL and Python for data validation, ensuring data accuracy and consistency. My approach involves setting clear criteria for data quality and regularly updating these standards to adapt to new data sources and business needs.

I prioritize data quality issues based on their impact on business operations and the potential risks they pose. Using a risk-based approach, I categorize errors into critical, high, medium, and low priority. Critical issues are addressed immediately to prevent operational disruptions, while lower priority issues are scheduled for regular maintenance cycles.

I am proficient in using tools like SQL, Python, and R for data analysis. For quality assurance, I utilize data validation tools such as Talend and Alteryx, which help in automating the data cleansing process. Additionally, I have experience with BI tools like Tableau and Power BI for visualizing data quality metrics.

I led a project to improve the quality of customer data for a retail company. I implemented a data governance framework and trained the team on best practices. The project resulted in a 30% reduction in data errors and improved customer satisfaction scores by 20%, demonstrating the direct impact of high-quality data on business outcomes.

I foster collaboration by establishing regular communication channels with other teams, such as IT and business units. I conduct workshops and training sessions to align everyone on data quality goals and processes. By involving stakeholders early and often, I ensure that data quality initiatives are supported and integrated into broader business strategies.