Senior Data Quality Analyst Salary
A Senior Data Quality Analyst earns an average salary of $71,020 per year. A skill in SQL is associated with high pay for this job. People in this job generally don't have more than 20 years' experience.
Job Description for Senior Data Quality Analyst
Senior data quality analysts are in charge of analyzing data quality for their organization. They are responsible for processing large amounts of information for other departments and making informed decisions related to the extracted data. They also create detailed data reports for upper management, as well as participate in business meetings to suggest new ideas for relevant processes. Senior data quality analysts lead a group of analysts to achieve all company objectives in a timely manner, using their data quality experience to coach and train junior analysts. Their main functions include tracking error logs, verifying the integrity of data, and reporting their progress to the data analysis supervisor or manager in their department. They also complete data abstraction and data entry into company databases, as well as conduct root cause analysis and make recommendations for improvements.Read More...
A bachelor's degree in a related field is required for this position. In addition, previous experience in a data quality analysis capacity is necessary. Senior data quality analysts must be proficient with office applications such as word processing and spreadsheet software. They should be responsible and organized individuals that pay close attention to detail and possess strong oral and written communication skills. Additionally, they should have an understanding of workflow design and data reusability. Senior data quality analysts also need strong analytical skills to solve problems as they arise, and they should be able to multitask in a variety of projects at a time.
Senior Data Quality Analyst Tasks
- Develop, implement, and evaluate data quality management policies and practices.
- Clean and scrub data, and implement data quality best practices.
- Develop prototypes and demonstrations of data quality and data integration practices.
- Participate in data quality investigations and analyses, and diagnose root causes and make recommendations.
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