Position: Senior QA Engineer – Backend Services, Automation & Performance Testing
Key Responsibilities
Design, implement, and execute comprehensive test strategies for backend services, covering functional, integration, automation, and performance testing.
Validate complex backend business workflows across distributed microservices, ensuring system reliability, data consistency, and service stability.
Develop and maintain scalable automation frameworks for API, integration, and database validation to support CI/CD pipelines.
Design and execute performance, load, stress, and endurance testing to evaluate system scalability, stability, and capacity under high transaction volumes.
Analyze performance bottlenecks using logs, metrics, distributed tracing, thread dumps, and database profiling, and provide optimization recommendations.
Validate data consistency and transaction integrity across databases, message queues, and downstream services.
Debug complex backend issues involving asynchronous processing, distributed transactions, and event-driven architectures.
Leverage AI-assisted software engineering tools to improve test design, automation development, defect analysis, root cause analysis, and overall engineering productivity.
Collaborate closely with engineering, product, DevOps, and architecture teams to ensure high-quality delivery of backend services.
Required Qualifications
Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
5+ years of QA experience focusing on backend services, distributed systems, or large-scale enterprise applications.
Strong hands-on experience in API automation, integration testing, database validation, and end-to-end backend testing.
Solid experience with performance testing tools such as JMeter, k6, Gatling, or equivalent.
Strong understanding of distributed microservices, REST APIs, asynchronous messaging systems (e.g., Kafka, RabbitMQ), and event-driven architectures.
Experience analyzing performance bottlenecks using monitoring platforms, distributed tracing, application logs, and database profiling.
Strong SQL skills with experience validating data consistency across multiple systems.
Proficiency in at least one programming language such as Java or Python.
AI-assisted software engineering experience is required, with demonstrated ability to leverage LLM-based tools (e.g., GitHub Copilot, Cursor, Claude Code, Codex, or similar) to improve testing efficiency, automation development, debugging, and root cause analysis.
Hands-on experience applying AI to software testing, including AI-assisted test case generation, automation development, intelligent defect analysis, or test workflow optimization.
Excellent debugging, troubleshooting, and root cause analysis skills for complex backend systems.
Experience with CI/CD pipelines and modern software development practices.
Fluent in spoken and written English.
Preferred Qualifications
Experience in fintech, banking, payment, lending, or other transaction-intensive systems.
Experience testing high-concurrency and high-availability backend services.
Familiarity with cloud-native technologies and observability tools such as Docker, Kubernetes, Prometheus, Grafana, ELK, or Jaeger.