Sr. QA Engineer

As a Senior QA Engineer, you will be the primary architect of our quality strategy and testing infrastructure. This role is a highly technical position designed for an engineer who thrives at the intersection of traditional SaaS (UI/API) and AI-native systems. You won't just be finding bugs; you will be building the automated systems that validate our distributed backbone, ensuring our GTM platform is resilient, scalable, and high-performing.

The core responsibilities for the job include the following:

Automation Architecture and Engineering (40%)

• Focus: Engineering the software frameworks that validate our core platform.
• Full-Stack Automation: Architect and maintain scalable end-to-end automation frameworks for our web UI, REST/gRPC APIs, and backend distributed services.
• Tooling Development: Write production-grade code in Golang, Python, or Java to build custom testing tools that simulate complex GTM workflows and high-concurrency environments.
• CI/CD and Release Integration: Design and manage automated quality gates within the deployment pipeline to ensure delivery of software without sacrificing stability.

Technical Leadership and Design Partnership (25%)

• Focus: Driving engineering excellence and cross-functional alignment.
• Architectural Review: Partner with software engineers during the design phase to ensure new features, from UI components to AI modules, are built with testability and scale in mind.
• Code Quality and Governance: Lead rigorous reviews of test code and contribute to application code reviews, providing feedback on maintainability, security, and edge-case handling.
• Mentorship: Actively guide junior engineers in adopting modern testing patterns and robust software design principles.

Reliability, Scaling, and Performance Testing (20%)

• Focus: Stress-testing the system and ensuring 24/7 reliability.
• Performance Engineering: Design and execute sophisticated load and stress tests to identify bottlenecks in our database modules and distributed services.
• Chaos and Resilience: Proactively simulate system failures and network partitions to validate the self-healing capabilities of our infrastructure.
• Root Cause Analysis (RCA): Lead deep-dive forensic analysis into complex, intermittent system failures, identifying architectural flaws and driving them to resolution.
• Functional Testing: Manually validate end-to-end workflows, including AI interactions within our platform. Write code to automate these test cases.

AI Validation and Data Integrity (15%)

• Focus: Validating the accuracy and integration of AI-native features.
• AI Module Verification: Develop modules to validate that AI-generated insights/responses are correctly integrated into the platform.
• Data Fidelity: Audit and verify the integrity of data pipelines that fuel our AI models, ensuring the platform operates on accurate, high-fidelity GTM data.
• Continuous Innovation: Stay at the forefront of industry trends in cloud-native testing and AI observability to keep our stack modern and competitive.

Requirements:

• Academic Background: A bachelor's degree or higher in computer science, software engineering, or a closely related highly technical field.
• Engineering Proficiency: Expert-level ability to write and debug code in Golang, Python, or Java within a Linux environment.
• Systems Knowledge: Strong experience with distributed systems and a deep understanding of how to test for race conditions and data consistency.
• Databases and APIs: Mastery of SQL/NoSQL query optimization and experience validating complex distributed database architectures.
• Analytical Communication: Proven ability to synthesize complex technical findings into actionable strategic insights for senior leadership.