Data Profiling and Assessment: Analyze data sources to understand their structure, content, and quality, identifying anomalies and patterns that may affect data integrity
Data Cleansing and Enrichment: Develop and implement processes to correct inaccuracies, remove redundancies, and fill in missing information to maintain high-quality datasets.
Data Quality Monitoring: Establish and monitor data quality metrics, creating dashboards and reports to track data quality trends and issues.
Root Cause Analysis: Investigate data quality problems to determine their origin and work with relevant teams to implement corrective measures
Process Improvement: Collaborate with cross-functional teams to enhance data collection, entry, and validation processes, thereby reducing the occurrence of data quality issues.
Data Governance: Contribute to the development and enforcement of data quality standards and policies, ensuring compliance with regulatory requirements
Source to Target testing , functional and integration testing
Preferred Tools
Quality automation tools . Query surge etc.