Data QA Analyst

• Design and execute test plans, cases, and scripts to validate data accuracy and integrity across systems.

• Conduct thorough data quality checks and audits to identify and resolve inconsistencies, errors, or anomalies.

• Collaborate with data engineering and development teams to ensure data pipelines meet quality standards.

• Develop and implement automated testing solutions for data validation and reconciliation.

• Analyze test results, identify root causes of data issues, and provide actionable recommendations for improvement.

• Maintain comprehensive documentation of test cases, results, and data quality processes.

• Assist in defining and implementing data quality metrics and reporting mechanisms.

• Stay updated on best practices and tools in data quality assurance and testing.

Technical Competency

• Bachelor s or master s degree in computer science, Information Technology, or a related field.

• Proficient in SQL and experience with querying large datasets for validation and analysis.

• Familiarity with data quality tools and frameworks such as Talend, Informatica, or similar.

• Hands-on experience with ETL processes and understanding of data warehousing concepts.

• Knowledge of scripting languages (Python, R, or similar) for data validation and testing automation.

• Experience with BI tools like Tableau, Power BI, or Qlik for data visualization and reporting.

• Strong understanding of database systems (e.g., MySQL, PostgreSQL, Oracle).

• Exposure to cloud platforms (e.g., AWS, Azure, Google Cloud) for data operations is a plus.

Functional Competency

• Strong attention to detail to ensure high levels of data accuracy and reliability.

• Excellent analytical and problem-solving skills to identify and resolve data-related issues.

• Ability to work collaboratively with cross-functional teams and communicate technical concepts effectively.

• Understanding of data governance principles and practices.

• Strong organizational and time-management skills to manage multiple tasks and deadlines.

• Proactive mindset with a focus on continuous improvement in data quality processes.