QA (Manual\Automation focus) project-based position
DUTIES AND RESPONSIBILITIES:
- Documentation Review: Assist in analyzing business goals, technical documentation, and law requirements to identify "logic holes," gaps, or missing edge cases before development starts.
- Infrastructure Monitoring: Audit Redis (cache/locks) and RabbitMQ (queues) to ensure no data is lost or stuck (AI tools may be used for analysis or command generation, but a fundamental understanding of how these systems operate is a must-have).
- SQL Data Verification: Use SQL (AI tools may be used for generation, but understanding the logic and execution is a must-have) to verify that "real" data in PostgreSQL matches business logic.
- Technical Reporting: Write high-quality technical bug reports that identify why a failure happened (e.g., a database deadlock, a message queue error, or a cache sync issue).
- Load Test Execution: Design load tests (AI tools may be used to generate scripts and scenarios, but a fundamental understanding of performance metrics and system behavior is a must-have) to simulate thousands of concurrent users and identify backend bottlenecks.
- Data Flow Monitoring: Trace the data flow from the Frontend (React/Next.js) to the Backend (NestJS) to ensure correct processing.
Requirements:
- Load Test Execution: Design load tests (AI tools may be used to generate scripts and scenarios, but a fundamental understanding of performance metrics and system behavior is a must-have) to simulate thousands of concurrent users and identify backend bottlenecks.
- Stack Knowledge: Basic understanding of frontend and backend concepts and architecture. (we use React/Next.js and NestJS)
- System Basics: Basic knowledge of microservices and how message brokers (e.g., RabbitMQ or Kafka) handle communication between services.
- Caching: Fundamental understanding of how distributed cache (e.g., Redis) is used for caching.
- Concurrency Awareness: The ability to visualize and test for complex Race Conditions (AI tools can assist in finding them, but you must understand the concept).
- Performance Testing: Basic experience or theoretical knowledge of performance tools such as K6, JMeter, or similar.
- AI-Native Workflow: Proven ability to use LLMs and AI coding assistants to generate test cases, analyze documentation, and audit backend source code.
- Shift-Left Mindset: A strong belief that quality starts at the "Idea" phase. You are proactive in preventing bugs during the design stage.
- Technical Communication: Ability to explain technical failures clearly to developers.