Junior Data Quality
Role details
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Tech stack
Job description
Amadeus is looking for talented Junior Data Quality to join our data teams and act as a key guardian of data quality across our data products. This role is essential to ensuring high standards in data delivery, from early requirement definition through to production release and ongoing monitoring. In this role you´ll work closely with data engineers, product owners, business stakeholders, and the wider quality community to define, implement, and continuously improve data quality controls. The role combines hands-on technical work, business understanding, and strong collaboration within an agile environment., Review, analyze, and challenge requirements from the earliest stages, applying a Shift Left quality philosophy. Create and execute test plans based on acceptance criteria to support story estimation, validation planning, and early detection of inconsistencies. Design, define, and maintain automated Data Quality Controls (DQC)-both technical and business-oriented; Organize, lead, and document workshops with stakeholders to understand how data is used, translating insights into relevant DQC metrics and test cases. Perform manual data validation within the agile delivery process and assess which tests should be automated. Support data releases by determining when and which DQC controls and manual tests must be executed. Monitor ongoing data ingestion using Power BI dashboards fed by DQC outputs, continuously improving metrics, controls, and visualizations. Support and guide data engineers and data leads on data pipeline testing and quality practices. Collaborate closely with Scrum teams and the wider quality and governance functions. Promote and enforce data governance and QA standards Identify inefficient or redundant practices in data pipelines and proactively propose improvements. About the Ideal Candidate
Requirements
Experience in data quality, data validation, or data testing roles within complex data environments. Understanding of data pipelines, data flows, and the end-to-end lifecycle of data products. Experience with SQL and Python, with confidence working in notebooks and version-controlled environments. Familiarity with cloud-based data platforms and analytics tools (e.g. data lakes, query engines, BI tools). Experience defining and implementing automated data quality checks and KPIs. Ability to translate business needs into measurable data quality rules and controls. Comfortable working in agile/Scrum teams and collaborating across technical and non-technical stakeholders. Strong communication skills, with the ability to lead workshops, document decisions, and influence good practices. A proactive mindset with a passion for improving data quality, governance, and delivery standards.