Senior QA Analyst – Cybersecurity Data & Databricks Testing

Be Part Of A High-Performing Team

Join the technology organization of a major global financial institution supporting highly regulated, security-sensitive enterprise environments. This opportunity sits within a cybersecurity data operations function responsible for ensuring that data warehouse platforms, security analytics, and reporting solutions deliver dependable information to business and technology stakeholders. The team operates in an Agile environment and works closely across data engineering, cybersecurity, development, and analytics functions.

• Engagement: W2 only (no C2C/1099)
• Hybrid opportunity in Charlotte, North Carolina
• Work on enterprise-scale cybersecurity data platforms and analytics solutions.
• Collaborate directly with data engineers, developers, cybersecurity stakeholders, and Agile delivery teams.
• Gain exposure to modern cloud data technologies including Databricks, Spark, Python, and enterprise data warehouse environments.

• Lead quality assurance efforts supporting enterprise cybersecurity data warehouse and dashboard solutions.
• Develop comprehensive test strategies, plans, cases, and scripts from business and technical requirements.
• Validate complex ETL/ELT pipelines, data transformations, and source-to-target mappings.
• Perform functional, integration, regression, system, performance, and user acceptance testing.
• Use advanced SQL and Spark SQL to perform data reconciliation, validation, and root-cause analysis.
• Build and execute automated data-validation solutions using Python, PySpark, and Pandas.
• Test REST APIs using Postman or comparable API-testing technologies.
• Identify, document, prioritize, and track defects while partnering with developers through resolution.
• Maintain requirement-to-test traceability and supporting QA documentation.
• Participate in Agile ceremonies and provide actionable feedback on system usability, data reliability, and platform stability.
• Help improve testing processes, automation practices, and overall QA standards across cybersecurity data initiatives.

• 8 years of experience across quality assurance, data testing, ETL/ELT testing, data engineering, or closely related disciplines.
• 5 years leading QA initiatives involving enterprise data warehouses, data lakes, or lakehouse environments.
• Strong hands-on experience with Databricks and testing data pipelines within cloud-based data platforms.
• Advanced SQL and Spark SQL capabilities, including complex data validation, reconciliation, and troubleshooting.
• Hands-on programming experience with Python, PySpark, and Pandas for automation, profiling, analysis, and data-quality validation.
• Demonstrated experience testing ETL/ELT pipelines, transformations, and source-to-target mappings.
• Hands-on experience validating REST APIs, preferably with Postman or a similar tool.
• Experience across functional, integration, regression, system, performance, and UAT testing.
• Strong defect-management, documentation, and requirements-traceability skills.
• Ability to collaborate effectively with engineers, developers, cybersecurity stakeholders, and Agile delivery teams.
• Strong analytical and root-cause problem-solving capabilities.