Department: Data, Insights & Artificial Intelligence
As a Data Quality Engineer, you will play a key role in the design, build, configuration, monitoring and continuous improvement of Data Quality Management products.
You will work closely with the Data Quality & Standards Product Manager, Business Stakeholders, Business Unit Data Stewards and IT partners (DTPS), ensuring that data quality controls, monitoring and reporting enable reliable and trusted data across the organization. This is a hands-on data engineering role requiring strong technical skills in SQL, PySpark and Azure-based data platforms, combined with full ownership of deliverables from design to production support and the flexibility to handle ad-hoc requests and parallel priorities while translating business requirements into scalable and automated data quality solutions.
Your New Key Responsibilities
Data Quality Controls
- Build, configure and maintain Data Quality controls in the Data Control Center.
- Translate business requirements into data quality rules, including logic definition, thresholds, exception handling and reduction of false positives.
- Ensure DQ controls are aligned with agreed business definitions and standards.
Monitoring, Operations & Issue Resolution
- Monitor DQ performance and ensure stable operations, including regular refresh cycles and system reliability.
- Troubleshoot failed checks, investigate data issues and perform root-cause analysis in collaboration with relevant stakeholders. - -- Support incident resolution and contribute to continuous operational improvements.
Continuous Improvement & Enhancement
- Implement improvements to existing DQ checks and contribute to the continuous enhancement of DCC capabilities.
- Identify opportunities to improve automation, scalability and reusability of controls and monitoring processes.
- Identify opportunities to automate monitoring and reduce manual effort for stakeholders.
Reporting & Dashboards
- Develop and maintain DQ dashboards and scorecards, enabling visibility of DQ KPIs, trends and drilldowns.
- Support stakeholders with insights and reporting to track performance and drive quality improvement actions.
Data Standards Operationalization
- Support data standards operationalization by ensuring alignment between published standards and DQ checks.
- Support adoption and compliance visibility by enabling reporting and transparency on standards adherence.
Collaboration & Delivery Support
- Work closely with the Product Manager and business stakeholders to clarify requirements, validate solutions and ensure high usability and adoption.
- Collaborate with DTPS and vendors to deliver end-to-end solutions, including integration dependencies and technical enablement. Run demos and workshops when needed to support rollout, stakeholder understanding and adoption.
- Testing, Release & Documentation Support release and testing activities, including UAT support and regression testing.
- Maintain basic documentation of implemented DQ checks, logic, definitions and changes.
Job Requirements
- 3+ years in data engineering / analytics engineering / data quality engineering Strong SQL and Python for scalable data validation and monitoring
- Experience working with Databricks and modern data pipelines (ADF or similar)
- Strong analytical and problem-solving mindset with attention to detail and quality.
- Ability to translate business requirements into technical implementation and measurable outcomes.
- Comfortable working across multiple stakeholders in a cross-functional environment.
- Strong communication skills and ability to explain technical topics to non-technical audiences.
- Ownership mindset: proactive, reliable and continuously improving solutions.
Required Technical Skills
SQL
Python
PySpark
Azure Databricks
Azure Data Factory
Power BI
NICE TO HAVE
Power Platform (Power Automate, etc.)
Microsoft Fabric
Copilot Studio
Experience working in a Data Mesh framework and/or domain data product mindset
Good awareness of key SAP MDG, MM, S/4HANA domains
