Data Quality Engineer

Enforce and maintain robust data quality standards and protocols. • Monitor and evaluate data quality metrics to identify and address anomalies and inconsistencies. • Participate in efforts to identify and address data-related errors and discrepancies promptly. • Collaborate with technical and non-technical teams to resolve issues and prevent future occurrences. • Develop and implement data quality assurance processes and procedures. • Design and execute data quality checks and validations to ensure accuracy, completeness, and consistency of data. • Collaborate with data engineers and data analysts to understand data pipelines and identify potential sources of data quality issues. • Investigate and report data quality issues in a timely manner. • Develop and maintain data quality metrics and reports to monitor and communicate the health of our data assets. • Develop, document and maintain manual and automation tests for ETL data pipelines using various frameworks like PyTest or Great Expectations. • Work closely with cross-functional teams to define data quality requirements and standards. • Provide technical expertise and guidance on data quality best practices. • Automate data quality monitoring and validation processes using Python and Databricks. • Perform data profiling and analysis to identify patterns and trends in data quality issues. • Stay up-to-date with industry trends and emerging technologies related to data quality management.