StarCompliance
Platform Quality Engineer (SDET)
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About StarCompliance
StarCompliance is on a mission to make compliance simple and easy. Trusted globally by enterprise financial institutions, the user-friendly STAR platform empowers organizations to achieve regulatory compliance while safeguarding their integrity and business reputations. Through a customizable, 360-degree view of employee activity, the STAR software enables firms to automate the detection and resolution of potential areas of conflict while streamlining daily workflows and increasing efficiency.
Role
The Platform Quality Engineer is a hands-on engineering role within StarCompliance’s centralised Platform Quality Engineering function, responsible for ensuring platform-level quality assurance across the enterprise SaaS platform.
Reporting to the Lead Quality Architect, the Platform Quality Engineer contributes directly to the design, implementation, and maintenance of automated quality controls that validate cross-service integration, core enterprise user journeys, and non-functional platform characteristics. The role is focused primarily on integration and API-level testing across a distributed, cloud-native system, including asynchronous and event-driven workflows.
StarCompliance’s enterprise platform is built on distributed services and cloud infrastructure, including messaging and integration components such as Azure Service Bus. The Platform Quality Engineer is expected to understand how these systems behave under load, failure, and recovery conditions, and how to test them to ensure reliability, resilience, and performance at scale.
The role places strong emphasis on validating failure modes, resilience, and recovery behaviour across distributed services, diagnosing complex system failures using logs, telemetry, and test data, and contributing to enterprise performance testing.
The Platform Quality Engineer works closely with Product Owners and engineering teams to understand upcoming platform changes and integrations, ensuring the enterprise platform is validated under realistic functional and load scenarios ahead of release.
Responsibilities
- Design, implement, and maintain automated tests primarily at the integration and API layers to validate behaviour across distributed services.
- Develop tests that validate asynchronous and event-driven workflows, including messaging, retries, ordering, and failure scenarios (e.g. Azure Service Bus-based integrations).
- Contribute production-quality code to shared automation frameworks and quality tooling used across the enterprise platform.
- Design and execute performance and scalability tests for the enterprise SaaS platform.
- Build and maintain performance test data, scenarios, and workloads that reflect real-world usage patterns, including message throughput and concurrency.
- Integrate automated functional and performance tests into CI/CD pipelines, contributing to reliable, high-signal quality gates.
- Support validation activities within the Staging environment as the primary platform integration and quality assurance layer.
- Analyse functional, integration, and performance test results to identify systemic platform risks and improvement opportunities.
- Partner with Product Owners to understand upcoming platform changes and integrations, preparing targeted integration and performance tests in advance.
- Collaborate with architecture, platform, and engineering teams to improve system testability, reliability, and observability.
- Apply AI-assisted engineering tools to accelerate test creation, improve coverage, analyse failures, and reduce test flakiness.
- Adhere to and contribute to quality standards, patterns, and engineering practices defined by the centralised Platform Quality Engineering function.
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Skills And Experience
- 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.
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Minimum Qualifications
- 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.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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