QA Analyst

We are seeking a highly skilled QA Engineer with 3 to 6 years of experience in ETL and data validation testing. The ideal candidate will be responsible for ensuring data integrity, accuracy, and consistency across multiple systems by performing comprehensive testing of ETL processes, data pipelines, and file processing mechanisms. The role requires strong analytical skills, hands-on experience with SQL and ETL testing tools, and the ability to work collaboratively with cross-functional teams including developers, data engineers, and business analysts. 

Key Responsibilities:

• Analyze, test, and validate ETL processes to ensure data accuracy, consistency, and completeness. 
• Develop and execute detailed test cases, test scripts, and SQL queries to verify data transformations and mappings. 
• Identify, document, and track defects, ensuring timely resolution and retesting. 
• Perform data reconciliation between source and target systems to verify correctness of ETL outputs. 
• Validate both incremental and full-load data processing workflows. 
• Test inbound and outbound file processing across multiple formats such as CSV, XML, and JSON. 
• Conduct integrity checks to ensure completeness and correctness of processed data and files. 
• Monitor and troubleshoot data ingestion or file loading failures. 
• Collaborate with developers and data engineers to ensure successful integration of files into databases and data warehouses. 
• Prepare and maintain clear documentation for test cases, results, and issue logs. 

Skills and Qualifications:

Technical Skills:

• Strong SQL querying and data analysis skills. 
• Proficiency with ETL testing tools and processes. 
• Experience working with file formats such as CSV, XML, and JSON. 
• Familiarity with scripting languages for automation (e.g., Python, Shell, etc.). 
• Knowledge of cloud platforms (AWS, Azure) is an added advantage. 

Soft Skills:

• Strong analytical and problem-solving abilities for troubleshooting ETL and data issues. 
• Excellent communication skills to explain data discrepancies, defects, and test outcomes. 
• Detail-oriented with the ability to document findings clearly and comprehensively.