Cybersecurity Japan Jobs (NOW HIRING)

The VP, Lead QA Engineer - Cyber Data Warehouse (CyberDW), is a critical role within the Information Security department and reports to the Director, Cyber Data Governance & Data Quality for the SMBC Americas Division.

This role is responsible for leading Quality Assurance (QA) testing efforts across the Cyber Data Warehouse (CyberDW) platform, ensuring the accuracy, integrity, reliability, and completeness of cybersecurity, risk, and operational data used for analytics, reporting, governance, and Continuous Controls Monitoring (CCM).

The successful candidate will establish and execute the CyberDW QA testing strategy, partnering closely with Cyber Security, Technology, Data Engineering, Data Governance, and Business stakeholders. The role will be responsible for validating data pipelines, ETL processes, data transformations, data products, dashboards, controls, and regulatory reporting outputs while ensuring adherence to enterprise quality standards.

As part of the overall cyber data modernization initiative, this individual will help build a scalable testing framework supporting the Cyber Data Lakehouse and establish best practices for functional testing, data testing, regression testing, automation, reconciliation, and release validation.

Note: This is a hybrid role, requiring the successful candidate to attend our Charlotte office. 

• Lead end-to-end Quality Assurance (QA) testing activities across the Cyber Data Warehouse (CyberDW) platform, ensuring delivery of high-quality, reliable, and scalable data solutions.
• Develop and maintain comprehensive test strategies, test plans, test cases, test scripts, and testing standards for CyberDW initiatives.
• Validate data ingestion processes, ETL/ELT pipelines, transformations, business rules, and data integrations across cybersecurity data domains.
• Perform source-to-target data validation, reconciliation testing, and data integrity testing to ensure accuracy, completeness, consistency, and timeliness of CyberDW data assets.
• Design, execute, and maintain functional, integration, regression, system, performance, and user acceptance testing (UAT) processes.
• Establish and implement automated testing frameworks and reusable test assets to improve testing efficiency, coverage, and consistency.
• Partner closely with Data Engineers, Data Governance, Information Security, and Technology teams to review requirements, identify test scenarios, and ensure solutions meet business and technical expectations.
• Identify, document, prioritize, and track defects through resolution, validating fixes and ensuring successful remediation before production deployment.
• Perform root cause analysis of data, application, and reporting issues, recommending corrective actions and process improvements.
• Validate dashboards, reports, metrics, and analytical outputs to ensure the accuracy of cybersecurity, risk, and operational reporting.
• Support Agile delivery processes by participating in backlog grooming, sprint planning, requirements reviews, and release validation activities.
• Define, monitor, and report on QA metrics, testing progress, defect trends, release readiness, and overall quality health to leadership and stakeholders.
• Ensure testing processes align with enterprise SDLC, risk management, audit, compliance, and cybersecurity standards.
• Maintain comprehensive test documentation, testing evidence, and audit-ready records to support regulatory and internal control requirements.
• Continuously improve QA methodologies, testing automation, and quality controls to enhance the overall maturity of the CyberDW testing program.
• Serve as the primary QA subject matter expert for CyberDW and provide quality sign-off recommendations for production releases.
• Establish and lead the CyberDW QA Center of Excellence (CoE), driving testing standards, best practices, governance, and continuous improvement across the platform.
•  Provide leadership and oversight for QA activities supporting strategic CyberDW initiatives, ensuring quality objectives are aligned with business priorities and risk management requirements.
•  Influence technology, engineering, and business stakeholders to embed quality practices throughout the software and data development lifecycle.

Required

• Bachelor's degree in Computer Science, Information Systems, Engineering, Data Analytics, or a related field.
• 8+ years of experience in Quality Assurance, Data Testing, ETL/ELT Testing, Data Engineering, or related disciplines.
• Hands-on experience validating large-scale data platforms built on Azure Databricks, including Delta Tables, notebook workflows, scheduled jobs, data pipelines, and API integrations.
• 5+ years of experience leading QA efforts for enterprise data warehouse, data lake, or lakehouse platforms.
• Strong hands-on experience with Databricks and testing data pipelines in cloud-based data platforms.
• Advanced proficiency in SQL and Spark SQL for complex data validation, reconciliation, and root-cause analysis.
• Hands-on experience with Python, PySpark, and Pandas for test automation, data validation, profiling, and analysis.
• Hands-on experience testing and validating REST APIs using Postman or similar API testing tools.
• Strong experience testing ETL/ELT pipelines, data transformations, and source-to-target mappings.
• Experience designing and executing functional, integration, regression, system, performance, and user acceptance testing.
• Experience creating automated testing frameworks and reusable test assets for data-centric applications.
• Strong understanding of software development lifecycle (SDLC), Agile methodologies, CI/CD practices, and release management processes.
• Extensive experience with data reconciliation, data quality validation, and defect management.
• Experience working in highly regulated environments, preferably within financial services, cybersecurity, or risk management domains.
• Strong analytical, problem-solving, and troubleshooting skills with the ability to identify root causes and drive resolution of complex data and application issues.
• Excellent verbal and written communication skills with the ability to collaborate effectively across business and technology teams.

Preferred

• Experience testing Power BI dashboards, reports, and analytical solutions.
• Knowledge of cybersecurity data domains including Identity & Access Management, Privileged Access Management, Vulnerability Management, Cloud Security, Threat Detection, Incident Response, and Security Operations.
• Exposure to Collibra Data Intelligence Platform (DIP), Data Governance, Data Lineage, Metadata Management, and Data Quality concepts.
• Experience with automated testing frameworks such as pytest, Great Expectations, dbt testing, Selenium, or equivalent technologies.
• Relevant certifications in Databricks, software testing, cloud technologies, or data management disciplines.