We are seeking a Senior QA Engineer with over 8 years of experience to lead our quality initiatives. This is a high-ownership role requiring a veteran who can architect automation frameworks, manage complex data environments, and integrate cutting-edge AI tools into the QA lifecycle. You will be the primary advocate for quality, ensuring our systems are scalable, reliable, and performant.

Key Responsibilities:

• Strategic Leadership: Define the long-term QA roadmap, balancing manual exploratory testing with advanced automation.
• Framework Architecture: Design and optimize Selenium-based frameworks that are maintainable and scalable across multiple projects.
• Complex Data Validation: Perform deep-dive backend testing using advanced SQL, including stored procedures, triggers, and performance tuning.
• AI Integration: Act as a subject matter expert in GitHub Copilot and MCP (Model Context Protocol) to automate repetitive tasks and accelerate test script development.
• Cross-Functional Collaboration: Work closely with DevOps and Product teams to ensure quality is "shifted left" in the development cycle.

Required Technical Skills:

• Experience: 8+ years in Software Quality Assurance with a heavy focus on automation.
• Automation Mastery: Expert-level proficiency in Selenium and at least one programming language (Java, C#).
• Database Expert: Advanced SQL skills for complex data verification and ETL testing.
• Modern Workflow: Hands-on experience using GitHub Copilot for code generation and familiarity with connecting MCP servers to LLMs for enhanced technical context.
• Ownership Mindset: Proven track record of taking full accountability for the quality of entire product modules or platforms.

Preferred (Bonus) Skills:

• GCP (Google Cloud Platform): Professional experience testing applications hosted on GCP, including knowledge of BigQuery, Cloud Functions, or Pub/Sub.
• Mentorship: Experience mentoring junior QA engineers and performing code reviews for automation scripts.
• Core Competencies
• Root Cause Analysis: Ability to not just find bugs, but to diagnose why they happened at the architectural level.
• Adaptive Learning: A proven ability to stay ahead of the curve (e.g., adopting AI tools like Copilot before they become industry standard).
• Communication: Ability to influence stakeholders and push for quality-focused technical debt resolution.