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.

Experience
7-11 yrs