About the job

We are seeking an experienced Data Quality Expert to drive data quality improvement, master data excellence, and AI-ready data foundations within a strategic global Quality IT system landscape. The role focuses on ensuring that data is complete, consistent, connected, and fit for advanced analytics and AI-driven use cases.

The role focuses on establishing transparent data quality monitoring, defining sustainable governance and processes, improving master data structures, and leveraging modern AI capabilities to identify and address data quality issues proactively.

The successful candidate will work closely with business stakeholders, system experts, process owners, master data owners, and project teams to establish a scalable data quality framework and enable AI-ready data foundations.

Contract Length: 3 months - likely to extendStart Date: Beginning of SeptemberLanguage: EnglishFully Remote

• Key Responsibilities Data Quality Strategy & Governance Assess and visualize current data quality status through dashboards, scorecards, and KPIs.
• Define data quality dimensions, business rules, and measurement methodologies.
• Establish sustainable governance models for data quality monitoring and continuous improvement.
• Define target states, success criteria, and measurable improvement goals with business stakeholders.
• Create transparent reporting mechanisms for management and operational teams.

• Data Analysis & AI-Driven Insights Analyze large datasets to identify quality issues, inconsistencies, duplicates, missing data, and process weaknesses.
• Apply modern AI and advanced analytics approaches to:
• Detect anomalies and patterns.
• Identify root causes of quality issues.
• Support proactive monitoring.
• Generate actionable recommendations.
• Evaluate emerging AI technologies and incorporate them into data quality processes and monitoring solutions.

• Master Data Management Identify critical master data objects and relationships.
• Collaborate with global master data owners to:
• Establish a Single Source of Truth.
• Define required master data structures and attributes.
• Harmonize data definitions and standards across systems.
• Ensure alignment between business requirements, data models, and system architecture.

• Data Modelling & Data Architecture Design and maintain conceptual, logical, and physical data models.
• Document data relationships, hierarchies, and dependencies.
• Support the definition of data standards and metadata requirements.
• Ensure data structures support business processes, reporting, AI use cases, and regulatory requirements.

• Process Design & Global Rollout Define global processes and standards for data creation and maintenance.
• Develop guidance, training materials, and supporting documentation.
• Support implementation and rollout activities globally across sites.
• Drive stakeholder engagement and adoption of new processes and standards.
• Facilitate workshops with SMEs, process owners, and business representatives.

• Interface & System Integration Support Define data requirements for interfaces and integrations.
• Support project teams during implementation and rollout of interfaces.
• Ensure data quality considerations are embedded in integration design.
• Collaborate with IT and system teams to improve data flows across the application landscape.

• Continuous Improvement Establish feedback mechanisms and workflows for reporting data quality issues.
• Identify opportunities to improve user experience and data entry quality.
• Lead root cause investigations and define corrective and preventive actions.
• Drive a culture of data ownership and data quality accountability.

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