Database QA Analyst

As an ideal candidate for this role, you should have the following qualifications: Role Overview: You must have experience in Snowflake data validation, particularly in a presentation/reporting layer hosted in AWS. Additionally, you should possess the ability to build reusable QA/testing frameworks and set up a Snowflake testing framework for reusing validation rules across various tables, reports, or datasets. Your expertise in Python and Pandas automation is crucial to automate validation checks, compare datasets, and minimize manual QA efforts. Moreover, you must be familiar with metadata/rules-based testing using Excel, YAML, or similar files to define validation rules that can be executed in Snowflake through a Python test engine, generating reports, logs, and alerts. Furthermore, strong SQL skills are essential for performing source-to-target testing and conducting row-level validation by writing queries to compare source data with target tables/views to identify discrepancies, gaps, duplicates, or transformation issues. Key Responsibilities: - Validate data in Snowflake, particularly in a presentation/reporting layer hosted in AWS - Build reusable QA/testing frameworks for Snowflake - Use Python and Pandas for automation of validation checks and dataset comparisons - Implement metadata/rules-based testing using Excel, YAML, or similar files - Utilize strong SQL skills for source-to-target testing and row-level validation Qualifications Required: - Snowflake data validation experience - Experience in building reusable QA/testing frameworks - Proficiency in Python and Pandas automation - Knowledge of metadata/rules-based testing - Strong SQL skills for source-to-target testing In addition to the job requirements, experience in API-based data validation and testing would be considered a plus. As an ideal candidate for this role, you should have the following qualifications: Role Overview: You must have experience in Snowflake data validation, particularly in a presentation/reporting layer hosted in AWS. Additionally, you should possess the ability to build reusable QA/testing frameworks and set up a Snowflake testing framework for reusing validation rules across various tables, reports, or datasets. Your expertise in Python and Pandas automation is crucial to automate validation checks, compare datasets, and minimize manual QA efforts. Moreover, you must be familiar with metadata/rules-based testing using Excel, YAML, or similar files to define validation rules that can be executed in Snowflake through a Python test engine, generating reports, logs, and alerts. Furthermore, strong SQL skills are essential for performing source-to-target testing and conducting row-level validation by writing queries to compare source data with target tables/views to identify discrepancies, gaps, duplicates, or transformation issues. Key Responsibilities: - Validate data in Snowflake, particularly in a presentation/reporting layer hosted in AWS - Build reusable QA/testing frameworks for Snowflake - Use Python and Pandas for automation of validation checks and dataset comparisons - Implement metadata/rules-based testing using Excel, YAML, or similar files - Utilize strong SQL skills for source-to-target testing and row-level validation Qualifications Required: - Snowflake data validation experience - Experience in building reusable QA/testing frameworks - Proficiency in Python and Pandas automation - Knowledge of metadata/rules-based testing - Strong SQL skills for source-to-target testing In addition to the job requirements, experience in API-based data validation and testing would be considered a plus.