Data Quality Engineer (Data QE)

Location: Hyderabad, India

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• Exclusive Opportunities: Access hidden job markets, visa guidance for international roles, and personalized career coaching. Join thousands of professionals who've advanced their careers through Vrinda International. Create your profile today and let's connect your talent to global opportunities! Summary We are seeking a highly skilled Data Quality Engineer to join our IT Services team. In this pivotal role, you will be responsible for ensuring the accuracy, consistency, and reliability of our data assets across complex environments. Your expertise will drive confidence in our reporting and analytics by implementing robust validation frameworks and maintaining strict data integrity standards. This position is critical for supporting data-driven decision-making and optimizing our overall data infrastructure. Responsibilities
• Design and execute comprehensive testing strategies for ETL processes to verify data transformation accuracy.
• Perform intricate data reconciliation activities to identify and resolve discrepancies between source and target systems.
• Conduct rigorous quality assurance checks to uphold data integrity across all operational databases.
• Optimize complex SQL queries to enhance performance and efficiency within data pipelines.
• Validate data structures and processes within PostgreSQL, Microsoft SQL Server (MSSQL), and Snowflake environments.
• Develop and maintain automated data quality testing suites to monitor system health proactively. Requirements

Requirements:

• Minimum of 6 years of professional experience in data engineering or quality assurance within the IT Services sector.
• Proven expertise in ETL testing, data validation methodologies, and reconciliation techniques.
• Strong proficiency in SQL query optimization and advanced database management.
• Hands-on experience with major data platforms including PostgreSQL, Microsoft SQL Server (MSSQL), and Snowflake.
• Demonstrated ability to perform data integrity assessments and implement quality control measures.
• Familiarity with data quality testing frameworks and tools.