W2: Senior QA Analyst – SMBC
Client: SMBC
Location: Charlotte, NC – Hybrid
Duration: 6+ Months
Pay Rate: Up to $50/hr W2
Technical Assessment
Candidates must complete the technical assessment before their resume is submitted.
Position Summary
We are seeking a Senior QA Analyst to support the Cyber Data Operations team within ISDAD. The candidate will be responsible for designing and executing test cases for CyberDW and Cyber Data Dashboards, identifying and tracking defects, and maintaining regression test plans, cases, and scripts.
The ideal candidate should have strong experience in QA testing, SQL/data analysis, JIRA, Agile/Scrum, and financial services/banking environments.
Key Responsibilities
Develop and execute test plans, test cases, and test scripts based on business and technical requirements.
Perform manual and automated testing across functional, regression, integration, and UAT phases.
Identify, document, track, and manage defects while collaborating with developers for resolution.
Ensure requirements traceability and maintain comprehensive QA documentation.
Participate in Agile/Scrum ceremonies with the CyberDW team.
Provide feedback regarding system usability, functionality, and stability.
Contribute to continuous improvement of QA processes, methodologies, and tools.
Identify, evaluate, and manage testing documentation and deliverables for projects.
Perform data validation, analysis, and reconciliation using SQL.
Required Skills & Qualifications
- 10+ years of experience in Software Testing, Quality Assurance, or Business Systems Analysis.
- Financial services or banking industry experience preferred.
- Strong hands-on experience with JIRA and test management tools.
- Experience with Confluence is a plus.
- Strong understanding of SDLC and Agile/Scrum methodologies.
- Strong analytical, problem-solving, communication, and documentation skills.
- Ability to scrub, analyze, and validate data.
- Strong SQL experience, including:
- Complex SQL queries
- Joins
- CTEs
- Data validation and reconciliation
- Identifying data anomalies/outliers
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