ETL QA Professional @ Infobeans

• Design, develop, and execute comprehensive ETL test cases to validate data extraction, transformation, and loading processes.
• Perform backend and data validation testing by writing and optimizing complex SQL queries.
• Validate data accuracy, integrity, completeness, and reconciliation between source and target systems.
• Identify, log, track, and retest defects using defect tracking tools, ensuring timely resolution.
• Collaborate closely with developers, data engineers, and business stakeholders to clarify requirements and ensure complete test coverage.
• Participate in Agile ceremonies, sprint planning, and project discussions.
• Be available for collaboration and meetings aligned with US EST working hours when required.

• Work on data-driven projects that directly impact business decisions and analytics outcomes.
• Collaborate with global teams across different time zones and technologies.
• Gain exposure to modern data platforms, including data warehouses and data lakes.
• Be part of a fast-paced, collaborative environment that values quality, ownership, and continuous improvement.
• Opportunity to enhance your skills across SQL, ETL processes, and cloud-based data ecosystems.

• Strong SQL expertise with hands-on experience in writing and optimizing complex queries.
• Proven experience in ETL Testing, including data extraction, transformation, and load validation.
• Solid understanding of data validation, reconciliation, and backend testing concepts.
• Experience working with SQL-based DBMS tools such as Oracle, SQL Server, Denodo, Azure Databricks, or similar platforms.
• Familiarity with data warehouses and data lakes.
• Knowledge of Agile methodologies and experience using defect tracking tools like JIRA.
• Strong analytical mindset with excellent attention to detail and problem-solving skills.
• Effective communication and collaboration skills in a distributed team setup.
• Willingness to work flexible hours to support cross-time-zone collaboration, including US EST.

Good-to-have skills

• Working knowledge of Python for data validation or automation.
• Exposure to cloud platforms such as AWS, Azure, or GCP and their data services.
• Experience in automation frameworks or scripting for ETL testing.