QA Engineer

About this role

CUBE are a global RegTech business defining and implementing the gold standard of regulatory intelligence for the financial services industry. We deliver our services through intuitive SaaS solutions, powered by AI, to simplify the complex and everchanging world of compliance for our clients.    Why us? 🌍 CUBE is a globally recognized brand at the forefront of Regulatory Technology. Our industry-leading SaaS solutions are trusted by the world’s top financial institutions globally. 🚀 In 2024, we achieved over 50% growth, both organically and through two strategic acquisitions. We’re a fast-paced, high-performing team that thrives on pushing boundaries—continuously evolving our products, services, and operations. At CUBE, we don’t just keep up we stay ahead. 🌱 We believe our future is built by bold, ambitious individuals who are driven to make a real difference. Our “make it happen” culture empowers you to take ownership of your career and accelerate your personal and professional development from day one. 🌐 With over 700 CUBERs across 19 countries spanning EMEA, the Americas, and APAC, we operate as one team with a shared mission to transform regulatory compliance. Diversity, collaboration, and purpose are the heartbeat of our success. 💡 We were among the first to harness the power of AI in regulatory intelligence, and we continue to lead with our cutting-edge technology. At CUBE, You will work alongside some of the brightest minds in AI research and engineering in developing impactful solutions that are reshaping the world of regulatory compliance. Key Deliverables — What Success Looks Like - Deliver scalable automation frameworks with comprehensive coverage across APIs, HTML, XML, PDF, and data processing pipelines. - Ensure high data quality through automated validation of content, metadata, schemas, and business rules. - Reduce regression defects and production issues through continuous automated testing. - Enable rapid feedback through CI/CD-integrated quality gates and automated reporting. - Deliver production-ready automation solutions that improve release confidence and engineering productivity. Key Responsibilities: - Design and develop automated test frameworks for backend services, APIs, and document processing pipelines. - Build automated validation for HTML, XML, JSON, PDF, and structured/unstructured data. - Implement data quality checks, schema validation, metadata validation, and content integrity verification. - Develop API, integration, regression, and end-to-end automated test suites. - Build continuous quality feedback loops integrated with CI/CD pipelines. - Develop automation scripts using Python and modern testing frameworks. - Integrate AI/ML and LLM-based tools for intelligent test generation, content comparison, defect analysis, and root cause identification. - Collaborate with Engineering, Product, DevOps, and Data Science teams to improve software quality and release reliability. Key Deliverables — What Success Looks Like - Deliver scalable automation frameworks with comprehensive coverage across APIs, HTML, XML, PDF, and data processing pipelines. - Ensure high data quality through automated validation of content, metadata, schemas, and business rules. - Reduce regression defects and production issues through continuous automated testing. - Enable rapid feedback through CI/CD-integrated quality gates and automated reporting. - Deliver production-ready automation solutions that improve release confidence and engineering productivity. First 90 Days — Objectives: Day 30: Understand the application architecture, document processing workflows, and quality standards. Deliver initial automated tests for APIs, HTML/XML/PDF validation, and backend services. Day 60: Expand automation coverage across data processing pipelines and implement automated data quality checks. Integrate validation, reporting, and quality gates into CI/CD pipelines. Day 90: Own end-to-end automation for document processing and backend services. Establish continuous feedback mechanisms and improve test coverage, data quality, and defect detection efficiency. Required Skills & Experience: - 3+ years of experience in QA Automation or Software Development in Test (SDET). - Strong programming skills in Python (preferred), Java, or JavaScript. - Experience with automation frameworks such as PyTest, Playwright, Selenium, Cypress, or Robot Framework. - Strong experience testing REST APIs, microservices, and distributed systems. - Experience validating HTML, XML, JSON, PDF, and document processing workflows. - Strong understanding of data quality validation, schema validation, metadata verification, and business rule testing. - Hands-on experience with CI/CD, Git, Docker, SQL, and automation pipelines. - Good understanding of backend systems, databases, and software development life cycle. Nice to Have: Experience with AI-assisted testing, LLM-based test generation, intelligent content validation, or automated document comparison. Exposure to Generative AI, NLP, or AI/ML applications. Experience with OCR, document processing, or content management systems. Knowledge of cloud platforms (AWS, Azure, or GCP), Kubernetes, and event-driven architectures. Experience with observability, quality analytics dashboards, and enterprise SaaS applications. Interested? If you are passionate about leveraging technology to transform regulatory compliance and meet the qualifications outlined above, we invite you to apply. Please submit your resume detailing your relevant experience and interest in CUBE. CUBE is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.