Description

• Contribute to the development and execution of data quality strategies and best practices to support the organization s data initiatives.
• ETL Testing: Design and perform thorough ETL tests, including test case development, data validation, transformation accuracy, end-to-end pipeline testing, and performance testing.
• AWS Data Pipeline Monitoring: Support the monitoring and optimization of AWS-based pipelines using services like Glue, S3, Athena, MSK, SQS, Lambda, Step Functions etc to ensure data accuracy, completeness, and reliability.
• Automation & Scripting: Develop Python-based scripts to automate data quality checks and validation processes.
• Collaboration: Work alongside data engineers, analysts, and business stakeholders to understand data flows and resolve data quality issues.
• Documentation: Maintain clear and detailed documentation of testing procedures, test results, and quality metrics.

Qualifications:

• 5+ years of experience in data quality engineering, data testing, or related fields.
• Strong hands-on experience with ETL testing and data validation techniques.
• Experience working with AWS data services, including Glue, S3, Athena, MSK, SQS, Lambda, Step Functions, etc.
• Proficiency in SQL and Python (or any other OOP languages like Java, C, C++, etc)
• Experience validating data in Power BI reports, including understanding of data models and report logic.
• Familiarity with test management tools such as Azure DevOps, Zephyr, or equivalent.
• Experience working in Agile environments (Scrum/Kanban).
• Exposure to data governance principles or data observability tools is a plus.
• Strong problem-solving skills and a proactive approach to identifying and resolving data quality issues.
• Good communication skills and the ability to work effectively with cross-functional teams.
• Continuous learning mindset and willingness to adapt to new technologies and techniques

Key Skills

Education

Bachelor's degree