Quality Engineer (SDET)

• Bachelor’s degree in Computer Science, Software Engineering, or a related technical discipline, or equivalent practical experience.
• Relevant certifications in cloud platforms (Azure preferred), performance testing, or quality engineering are beneficial but not required.

AI

• At StarCompliance, we’re dedicated to leading the way in innovation by seamlessly integrating AI into our offerings. We encourage our team to fully embrace these advanced tools, empowering us to work more efficiently, create with greater precision, and deliver exceptional value to our clients.

Integrity and Ethics

• All StarCompliance employees are expected to commit to a high standard of personal integrity and carry out their responsibilities in an ethical manner

Originally posted on Himalayas

• Demonstrable experience in platform- or enterprise-scale SaaS application quality assurance, with a focus on validating integration, non-functional characteristics, and release readiness across distributed systems.
• Strong coding skills in one or more of C#, TypeScript, or Python, with the ability to write clean, maintainable, production-quality code.
• Demonstrated experience designing and maintaining automated tests at the integration and API layers for distributed, cloud-native systems.
• Solid understanding of cloud-native architectures and how to test them, including messaging, asynchronous processing, retries, ordering, and failure handling
• Hands-on experience testing event-driven systems using cloud messaging or integration technologies (e.g. Azure Service Bus, Kafka, SNS/SQS, or similar).
• Strong hands-on experience with performance testing tools and approaches (e.g. k6, JMeter, Gatling, or similar).
• Experience designing realistic performance test scenarios and generating representative test data, including load, concurrency, and message-driven workloads.
• Practical experience integrating functional and performance testing into CI/CD pipelines (Azure DevOps preferred).
• Experience diagnosing complex system failures using logs, telemetry, and test data.
• Hands-on experience using AI-assisted engineering tools to improve productivity, test quality, or failure analysis.
• Strong analytical and problem-solving skills, with the ability to reason about system behaviour and failure modes in distributed environment