Data Testing Expert

Requirements

• Work Experience
• 2+ years of solid QA/Testing/Development experience
• 2+ years of Advanced SQL experience
• 2+ years of Data Automation experience
• Level of Knowledge
• Bachelor's Degree in Computer Science, Computer Engineering, Information Systems or related degree
• Strong Advanced SQL knowledge
• Strong Scripting and Test Automation knowledge
• Strong understanding of tools (test automation, jira, git, jenkins) and data quality policies and procedures
• Proficiency in scripting languages (e.g., Python, SQL) and automation tools
• Equal Opportunity Employer
• Globe's hiring process promotes equal opportunity to applicants, Any form of discrimination is not tolerated throughout the entire employee lifecycle, including the hiring process such as in posting vacancies, selecting, and interviewing applicants.
• Globe's Diversity, Equity and Inclusion Policy Commitment can be accessed here
• Make Your Passion Part of Your Profession. Attracting the best and brightest Talents is pivotal to our success. If you are ready to share our purpose of Creating a Globe of Good, explore opportunities with us.

Benefits

Additional Information

At Globe, our goal is to create a wonderful world for our people, business, and nation. By uniting people of passion who believe they can make a difference, we are confident that we can achieve this goal. Job Description Data Testing Expert is responsible validating data sources and ingestion pipes, data integration and extraction processes, testing transformation logic, and ensuring the correctness of target tables and files. Must possess strong technical capabilities, including proficiency in coding languages like Python and SQL, and be adept at troubleshooting complex technical issues. Role is to safeguard quality throughout the pipeline, wielding analytical acumen and problem-solving skills. DUTIES AND RESPONSIBILITIES: Data Testing: Conduct thorough testing of data flows, pipelines, and integration points to identify discrepancies, errors, and data quality issues. Data Validation: High level verification of data accuracy, completeness, and consistency throughout the data journey, from source to destination. Test Automation: Develop and maintain automated test scripts and frameworks to streamline data flow testing processes. Documentation: Create and maintain detailed documentation of test cases, test results, and issues discovered during testing. Performance Testing: Conduct performance testing of data flows to assess system scalability, response times, and resource utilization. Regression Testing: Perform regression testing to ensure that data flow changes or updates do not introduce new issues or negatively impact existing functionality.

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