I have experience with tools like SQL for querying databases, Python for data cleaning and analysis, and Tableau for visualizing data quality metrics. Additionally, I’ve used specialized tools like Informatica Data Quality and Talend to automate data validation and ensure accuracy. These tools help me identify inconsistencies, duplicates, and missing data, enabling me to maintain high-quality datasets.

I implement data validation rules and cross-check data across sources to ensure consistency. I also use ETL processes to standardize data formats and perform regular audits. By collaborating with stakeholders, I align data definitions and resolve discrepancies, ensuring a unified and accurate dataset for decision-making.

In a previous role, I discovered a recurring issue with duplicate customer records due to a flawed integration process. I analyzed the root cause, implemented deduplication rules, and automated the process using Python scripts. This reduced errors by 30% and improved the reliability of customer data for marketing campaigns.

I prioritize tasks based on their impact on business outcomes and urgency. Critical issues affecting decision-making or compliance are addressed first. I also use project management tools to track progress and communicate with stakeholders to manage expectations while ensuring high-quality deliverables.

I use metrics like accuracy, completeness, consistency, timeliness, and uniqueness. For example, I measure accuracy by comparing data against trusted sources and completeness by checking for missing values. These metrics help me quantify data quality and identify areas for improvement.