Job Opportunities API

Why Keyrus, Why Now! Keyrus is an international group of 2,800 consultants and experts across 28 countries, built on a single conviction: AI does not transform businesses. Architected intelligence does. For more than 30 years, we have been building the data foundations that make intelligent systems work — designing the Operating System of the intelligent enterprise, where intelligence is embedded into the core of business processes to create sustainable value: we operationalise intelligence. AI does not replace humans. It repositions us to a place no system can follow: understanding, deciding, designing, and creating. At Keyrus, you will not just develop skills—you will develop judgment. Your expertise sharpens with every client challenge you solve, every opportunity you shape, and every strategic partnership you help build. Over time, you grow into one of the rarest professionals of the intelligence era: someone who bridges business strategy, data, AI, and executive decision-making at scale. This is not a role you fill. It is a discipline you master and a story you help write to become a Keyrus Architect of Intelligence. Technology amplifies. Keyrus culture differentiates. Industrial discipline connects the two. 🚀 What You'll Architect As a Quality Engineer within Keyrus's Enterprise Data Platform practice, you will architect the trust behind the data — building the validation frameworks, testing automation, and quality controls that ensure enterprise data products are accurate, reliable, and ready to drive business decisions. You will operate across ingestion, transformation, and processing layers of complex cloud analytics environments, working shoulder-to-shoulder with data engineers and architects to embed quality into the core of every pipeline, not as an afterthought but as an engineered discipline. You will: Validate data across ingestion, transformation, integration, and processing workflows to ensure quality, accuracy, and consistency Perform source-to-target validation and reconciliation across multiple systems and data platforms Verify business rules, data mappings, transformations, and data lineage throughout the data lifecycle Ensure compliance with key data quality dimensions, including completeness, accuracy, consistency, timeliness, validity, uniqueness, and referential integrity Design, develop, execute, and maintain automated and manual testing frameworks for data validation Implement reusable quality controls and automated testing processes integrated into CI/CD pipelines Create and maintain test cases, validation rules, test scenarios, and quality documentation Continuously identify opportunities to automate manual testing and improve testing efficiency Support enterprise data platforms built on modern cloud technologies and large-scale data ecosystems Validate data pipelines, orchestration workflows, scheduling processes, and integration components Monitor pipeline execution and quality controls to proactively identify and resolve issues Collaborate with engineering teams to ensure the successful deployment of new data products and platform capabilities Investigate data discrepancies, pipeline failures, and quality issues; perform root cause analysis Manage defects through their full lifecycle, from identification and documentation through validation and closure Conduct regression testing to ensure new changes do not negatively impact existing functionality Participate in User Acceptance Testing (UAT) activities in partnership with business and technical teams Support release readiness reviews, quality gate assessments, and post-release validation Contribute to the establishment and continuous improvement of quality standards, testing methodologies, and best practices 🧠 Who You Are You see data quality as the foundation of trust, not a checkbox at the end of the pipeline You naturally connect technical validation with