Responsible:

• Contributing towards the defining of test strategies and test planning
• Validating data models, reports, and dashboards against source systems to ensure data accuracy and consistency with defined business rules
• Executing test cases for data pipelines, ETL processes, and data transformations across the analytics lifecycle (data ingestion to reporting)
• Contributing towards the development of Python-based automation frameworks for regression and data validation testing
• Preparing and maintaining test cases, test data, and execution results for analytics datasets and dashboards
• Performing data validation checks, including data completeness, transformation accuracy, and aggregation logic
• Assisting in identifying and documenting data issues and supporting root cause analysis activities
• Following established QE standards and ensuring proper documentation and traceability of testing activities in Jira and Confluence
• Participating in sprint activities, including planning, stand-ups, and retrospectives

Requirements:

• A Bachelor’s Degree in Computer Science, Information Technology, Data Science, or a related field
• 1–3 years of experience in Software or Data Quality Engineering (ETL/BI testing exposure is a plus)
• Good knowledge of SQL, with the ability to write and validate queries
• Basic to intermediate knowledge of Python for test automation or data validation
• A good understanding of software testing principles and QA methodologies
• Experience working in Agile environments and familiarity with tools like Jira and Confluence
• Exposure to cloud data platforms (e.g., GCP) is an added advantage
• Strong analytical and problem-solving skills
• Good communication and collaboration skills to work effectively with cross-functional teams
• The willingness to learn and grow in data engineering and quality engineering practices
• Attention to detail and commitment to delivering quality work