Quality Engineer – Data Quality

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 by building validation frameworks, automated testing solutions, and quality controls that ensure enterprise data products are accurate, reliable, and ready to drive business decisions.

You will operate across ingestion, transformation, storage, and processing layers of complex Google Cloud Platform (GCP) analytics environments, working closely with Data Engineers, Cloud Architects, and Data Analysts to embed quality throughout the entire data lifecycle.

You will:

• Validate data across ingestion, transformation, integration, and processing workflows in GCP environments to ensure quality, accuracy, and consistency.

• Perform source-to-target validation and reconciliation across multiple systems, including BigQuery, Cloud Storage, Cloud SQL, and external data sources.

• Verify business rules, data mappings, transformations, and data lineage throughout cloud-based data pipelines.

• 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 across GCP data ecosystems.

• Implement reusable quality controls and automated testing processes integrated into CI/CD pipelines using tools such as Cloud Build, GitHub Actions, GitLab CI/CD, or similar platforms.

• Create and maintain test cases, validation rules, test scenarios, quality metrics, and testing documentation.

• Continuously identify opportunities to automate manual testing processes and improve testing efficiency.

• Support enterprise data platforms built on Google Cloud Platform, including BigQuery, Dataflow, Cloud Composer (Apache Airflow), Pub/Sub, Dataproc, and Cloud Storage.

• Validate ELT/ETL pipelines developed using modern cloud-native data engineering frameworks.

• Monitor pipeline execution, orchestration workflows, scheduling processes, and quality controls to proactively identify and resolve issues.

• Collaborate with Data Engineering and Cloud teams to ensure successful deployment of new data products and platform capabilities.

• Investigate data discrepancies, pipeline failures, and quality issues through 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 data assets and analytics products.

• Participate in User Acceptance Testing (UAT) activities with business stakeholders and technical teams.

• Support release readiness reviews, quality gate assessments, and post-release validation activities.

• Contribute to the establishment and continuous improvement of enterprise data 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 business impact, understanding what "accurate" really means for decision-makers

• You are comfortable engaging engineers, architects, and business stakeholders to align on data