Data Test Engineer

As a Test Engineer Data, you will be responsible for owning the end-to-end data validation strategy for the migration process. Your main goal is to ensure that every model in the new Snowflake/dbt stack is equivalent to the legacy SQL Server/SSIS/Excel baseline before any cutover decision is made, acting as the quality gate between legacy decommission and production go-live. Key Responsibilities: - Design and maintain the migration validation framework, including row-count reconciliation, aggregate hash comparison, and business-rule assertion libraries. - Develop automated reconciliation scripts (Python + SQL) to compare SQL Server source outputs with Snowflake Silver and Gold layer equivalents. - Define an end-to-end testing strategy for data ingestion, transformation, metrics, and reporting. - Create and execute test cases for source-to-target reconciliation, business rules, and output validation. - Validate the completeness, accuracy, freshness, and consistency of data across the target platform. - Support various testing phases such as system integration testing, user acceptance testing, regression testing, and cutover readiness. - Identify, document, and track defects through resolution. - Implement and maintain dbt test suites using schema tests and custom SQL assertions. - Define data quality SLAs by domain and ensure adherence. - Manage the cutover readiness checklist to ensure a signed-off artifact before any production cutover window. - Set up ongoing data quality alerting post-go-live using dbt test failures. - Build and maintain a reconciliation dashboard in Sigma for business stakeholders. - Conduct root-cause analysis on data quality failures found during UAT. - Document test coverage gaps and escalate untested business rules for model updates. Qualifications Required: - Experience with data migration testing, reconciliation, and data quality validation. - Strong SQL and analytical validation skills. - Experience with modern cloud data platforms and reporting solutions. - 4+ years in data engineering or analytics engineering focusing on data quality and testing. - Proficiency in Python for scripting reconciliation jobs and generating validation reports. - Attention to detail in financial data understanding decimal type handling, rounding, and floating-point risks. - Ability to collaborate with technical teams and business users. This job also requires experience with programmatic data quality frameworks, familiarity with Snowflake TIME TRAVEL, and a background in financial services or regulated industry data validation would be beneficial. As a Test Engineer Data, you will be responsible for owning the end-to-end data validation strategy for the migration process. Your main goal is to ensure that every model in the new Snowflake/dbt stack is equivalent to the legacy SQL Server/SSIS/Excel baseline before any cutover decision is made, acting as the quality gate between legacy decommission and production go-live. Key Responsibilities: - Design and maintain the migration validation framework, including row-count reconciliation, aggregate hash comparison, and business-rule assertion libraries. - Develop automated reconciliation scripts (Python + SQL) to compare SQL Server source outputs with Snowflake Silver and Gold layer equivalents. - Define an end-to-end testing strategy for data ingestion, transformation, metrics, and reporting. - Create and execute test cases for source-to-target reconciliation, business rules, and output validation. - Validate the completeness, accuracy, freshness, and consistency of data across the target platform. - Support various testing phases such as system integration testing, user acceptance testing, regression testing, and cutover readiness. - Identify, document, and track defects through resolution. - Implement and maintain dbt test suites using schema tests and custom SQL assertions. - Define data quality SLAs by domain and ensure adherence. - Manage the cutover readiness checklist to ensure a signed-off artifact before any production cutover window. - Set up ongoing data quality alerting post-go-live using dbt test failures. - Build and maintain a reconciliation dashboard in Sigma for business stakeholders. - Conduct root-cause analysis on data quality failures found during UAT. - Document test coverage gaps and escalate untested business rules for model updates. Qualifications Required: - Experience with data migration testing, reconciliation, and data quality validation. - Strong SQL and analytical validation skills. - Experience with modern cloud data platforms and reporting solutions. - 4+ years in data engineering or analytics engineering focusing on data quality and testing. - Proficiency in Python for scripting reconciliation jobs and generating validation reports. - Attention to detail in financial data understanding decimal type handling, rounding, and floating-point risks. - Ability to collaborate with technical teams and business u