Group Data Quality Engineer

🚀We're Hiring for an exciting new opportunity: Group Data Quality Engineer

(Portugal - Full Remote)

*Mandatory to be in Portugal*

What you will do?

• Design, implement and continuously improve group-wide data quality controls for Sales and Finance data products, covering completeness, accuracy, consistency, validity, timeliness and uniqueness.
• Translate business rules and requirements into measurable data quality rules, automated tests, monitoring and alerting.
• Build and maintain data quality dashboards, scorecards, issue logs and service-level indicators that provide transparent reporting across countries and business functions.
• Analyse data anomalies and recurring quality issues, perform root-cause analysis and coordinate sustainable remediation with data owners, source-system teams and data product teams.
• Embed automated data quality checks into data pipelines and development processes, so defects are identified early and prevented from reaching downstream reports, analytics or AI use cases.
• Define and maintain critical data elements, validation standards, quality thresholds, ownership and escalation paths in line with the group data governance framework.
• Partner with Sales and Finance stakeholders to improve the reliability of customer, lead, revenue, volume, pricing, cost, profitability and financial reporting data.
• Contribute to shared engineering standards, documentation and reusable data quality components across the decentralised group data platform.

Requirements (Mandatory):

• Degree in computer science, business informatics, data engineering, mathematics, finance, industrial engineering or a comparable field.
• Several years of practical experience in data quality, data engineering, analytics engineering or a comparable data-focused role.
• Strong SQL skills and hands-on experience with data profiling, validation, reconciliation, root-cause analysis and automated testing.
• Experience with modern cloud data platforms and data pipelines, ideally Snowflake, including data modelling and transformation frameworks.
• Knowledge of data quality dimensions, controls, monitoring approaches, metadata, lineage and data governance principles.
• Understanding of Sales and Finance processes and data, such as customer and lead management, revenue, pricing, costs, profitability, planning and financial reporting.
• Experience with scripting or programming, preferably Python, and with version control, continuous integration and deployment practices.
• Ability to convert complex business definitions into clear, testable rules.
• Practical experience leveraging AI tools to support data profiling, quality analysis, anomaly detection, root-cause analysis, documentation and efficient delivery of data engineering tasks.
• Very good English skills.