Senior QA Testing Specialist -Data Engineer

SMBC Group is a top-tier global financial group headquartered in Tokyo with a 400-year history, offering a diverse range of financial services including banking, leasing, securities, credit cards, and consumer finance. The Group operates over 130 offices and employs 80,000 people worldwide across nearly 40 countries. In the Americas, SMBC Group serves corporate, institutional, and municipal clients with commercial and investment banking services, leveraging its extensive global network and capital strength.

About the role

The Senior QA Testing Specialist- Data Engineer ensures the accuracy, reliability, and integrity of enterprise data platforms built on Databricks. This role combines data engineering knowledge with quality assurance expertise to validate complex ETL pipelines, data transformations, and large-scale datasets. The position partners closely with Data Engineers, Business Analysts, Product Owners, and Project Managers across globally distributed teams to enable data-driven decision-making.

Responsibilities

• Perform end-to-end data validation, including source-to-target mapping, reconciliation, and completeness checks.
• Validate data transformations across Databricks Medallion Architecture (Bronze, Silver, Gold).
• Ensure data quality standards are met prior to production releases.
• Participate in requirements analysis and solution design reviews.
• Define test scope, scenarios, risk-based coverage, and overall test strategies.
• Design and execute test cases for complex ETL pipelines and perform regression testing.
• Write and execute advanced SQL queries (CTEs, window functions, complex joins).
• Develop Python validation scripts using PySpark and Pandas to support and enhance data test automation.
• Monitor Databricks jobs and workflow executions; investigate pipeline failures, data inconsistencies, and quality issues.
• Analyze defects both functionally and technically prior to assignment.
• Log, track, and manage defects using JIRA, ensuring high-quality defect documentation.
• Collaborate closely with Data Engineers, Architects, and Business teams to resolve issues.
• Provide clear and timely communication on quality risks, test progress, and release readiness.
• Escalate issues through defined governance and quality escalation channels.

Requirements

• 8+ years of experience in Quality Assurance testing or relevant Data Engineering development roles.
• Demonstrated expertise in Advanced SQL, including CTEs, window functions, and complex joins, for large-scale data validation.
• 6+ years of experience in test management strategy; experience in the securities or financial services industry.
• 3+ years of experience in designing solutions in process flow, business logic, and UI.
• Strong experience within the Azure data ecosystem, with hands-on expertise in Databricks notebooks and cluster management.
• Experience building or supporting data test automation frameworks, with exposure to CI/CD pipelines and Git-based version control.
• Hands-on manual and automated test development and design experience leading medium to large-scale or enterprise-wide projects.
• Strong command of Python, particularly PySpark and Pandas, for comprehensive data validation and analysis.
• Thorough understanding of ETL processes, data pipelines, and data warehouse architecture, with proven experience in ETL/Data Warehouse testing, including end source-to-target validation.
• Prior experience working directly with business users and finance stakeholders, including exposure to Oracle or similar enterprise financial platforms.
• Strong analytical, critical-thinking, and problem-solving skills.
• Excellent written and verbal communication skills, with the ability to articulate technical concepts clearly.
• Ability to work independently while collaborating effectively across distributed and global teams.

Measures of Success

• High level of confidence in production data quality, reflected by reduced defect leakage post-release.
• Strong and consistent test coverage across complex, multi-layer data platforms.
• Effective collaboration with engineering, product, and business teams throughout the delivery lifecycle.
• Proactive identification and mitigation of data quality risks and issues.
• Consistent, on-time delivery of test outcomes without compromising quality standards.

Schedule

SMBC’s employees participate in a Hybrid workforce model that requires employees to live within a reasonable commuting distance of their office location. Hybrid work may not be permitted for certain roles, including certain FINRA-registered positions requiring full-time in-office attendance.