DB Tester

Your Role

The successful candidate will be responsible for the following:- Must know Japaneese·        

• Design, build, and maintain automated test frameworks for data integration pipelines, transformation processes and AI/ML models.·        
• Define and govern evaluation datasets, golden data, and quality benchmarks for data and AI systems.·        
• Develop and execute comprehensive test strategies to validate data accuracy, completeness, consistency, lineage, model bias/fairness, and regulatory compliance.·        
• Build and automate end-to-end evaluation pipelines for large-scale data and AI workflows (batch/streaming where applicable).·        
• Partner with cross-functional teams (Data Engineering, App Dev, Platform, Security, Risk/Compliance, and Business) to define QA strategy and ensure alignment with requirements and acceptance criteria.·         Monitor, analyze, and report key quality metrics, including:Ø  Data quality indicators (e.g., freshness, validity, reconciliation, SLA adherence)Ø  Evaluation coverage (datasets, scenarios, edge cases)Ø  Model performance (task-specific KPIs, drift)Ø  Hallucination and bias rates (where applicable)Ø  Compliance incident rates and audit findings·      
• Implement advanced QA capabilities such as anomaly detection, automated data validation, model behavior testing, and robustness/guardrail evaluations.·        
• Ensure adherence to data governance, privacy, and security standards (access controls, PII handling, retention, auditability).·        
• Drive adoption of best practices across CI/CD, DevOps, MLOps, and test automation (quality gates, versioning, reproducibility).·        
• Stay current with evolving AI technologies, regulatory expectations, and industry QA standards; translate them into actionable engineering practices.·        
•  Champion a culture of quality, innovation, and continuous improvement through standards, playbooks, and coaching across engineering teams.

Your Profile

The successful candidate will be responsible for the following:- Must know Japaneese·        

• Design, build, and maintain automated test frameworks for data integration pipelines, transformation processes and AI/ML models.·        
• Define and govern evaluation datasets, golden data, and quality benchmarks for data and AI systems.·        
• Develop and execute comprehensive test strategies to validate data accuracy, completeness, consistency, lineage, model bias/fairness, and regulatory compliance.·        
• Build and automate end-to-end evaluation pipelines for large-scale data and AI workflows (batch/streaming where applicable).·        
• Partner with cross-functional teams (Data Engineering, App Dev, Platform, Security, Risk/Compliance, and Business) to define QA strategy and ensure alignment with requirements and acceptance criteria.·         Monitor, analyze, and report key quality metrics, including:Ø  Data quality indicators (e.g., freshness, validity, reconciliation, SLA adherence)Ø  Evaluation coverage (datasets, scenarios, edge cases)Ø  Model performance (task-specific KPIs, drift)Ø  Hallucination and bias rates (where applicable)Ø  Compliance incident rates and audit findings·      
• Implement advanced QA capabilities such as anomaly detection, automated data validation, model behavior testing, and robustness/guardrail evaluations.·        
• Ensure adherence to data governance, privacy, and security standards (access controls, PII handling, retention, auditability).·        
• Drive adoption of best practices across CI/CD, DevOps, MLOps, and test automation (quality gates, versioning, reproducibility).·        
• Stay current with evolving AI technologies, regulatory expectations, and industry QA standards; translate them into actionable engineering practices.·        
•  Champion a culture of quality, innovation, and continuous improvement through standards, playbooks, and coaching across engineering teams.