Senior QA Data Quality Engineer

As a Senior QA Data Quality Engineer, you will lead quality assurance initiatives and collaborate with cross-functional technology teams to ensure the delivery of scalable, high-performance applications and platforms.

The candidate will be responsible for building and maintaining automated test solutions to ensure high-quality software delivery.

This is a technical, delivery-focused role requiring an understanding of modern automation practices, the ability to leverage AI-assisted development tools, and a commitment to maintaining high quality standards within complex data environments.

In this role, you will

• Responsible for overall quality of testing deliverables/activities.
• Execute complex data validation strategies, focusing on Snowflake or SQL-based data integrity.
• Develop and maintain basic Python scripts to automate testing tasks and streamline regression cycles.
• Integrate and manage automated workflows within CI/CD pipelines.
• Collaborate with technical teams to troubleshoot data discrepancies and ensure the robustness of the data ecosystem.
• Responsible for designing effective test cases to bring test optimization.
• Leverages the existing automation frameworks, tools and artifacts to ensure the testing process is continuous, comprehensive, and fully autonomous.
• Support the implementation of the QE Program and overall QE process and standards through continuous test execution and reporting.
• Closely work with the Application Development team as one team to integrate quality engineering mindset/concepts within the DevOps framework/pipeline.

Required skills

• Overall 6-10 years of experience in Python, Javascript, SQL and Unix testing platform, code reviews prior to code deployments, integration test automation.
• Automation & Scripting : Ability to create and update automation scripts using Python.
• Database Testing: Strong expertise in DB testing, specifically using Snowflake or advanced SQL.
• CI/CD Integration: Experience working with Airflow, Jenkins, or similar orchestration tools.
• QE SDLC: Good understanding of the full QE SDLC, including requirements gathering, test case/scenario creation, execution, and formal documentation.
• AI Integration: Practical exposure to using AI tools like Copilot or Cortex AI for code generation and efficiency.