Data Quality Specialist

votre rôle

Mission / role purpose:

The Data Quality Specialist is responsible for ensuring the accuracy, integrity, and reliability of the organization’s master data. This role combines analytical expertise and governance oversight to identify data quality issues, define and implement KPIs, and support continuous improvement initiatives. By collaborating closely with data owners and stakeholders, the specialist enables trusted, high-quality data to drive effective decision-making and operational excellence across the enterprise.

key accountabilities

Data Quality Management:
Ensure high data quality by performing data analysis, profiling, and continuous monitoring to detect and resolve data quality issues promptly.
Develop, maintain, and enforce data quality standards, policies, and procedures to support consistent and reliable data across the organization.
Collaborate with stakeholders to define data quality metrics and targets, taking full ownership of KPI implementation, monitoring, and reporting across all relevant data domains. 

Data Governance:
Implement defined data governance framework with respect to Master Data, including processes, roles, and control mechanisms.
Define and manage data quality rules, data lineage, and data classification to ensure transparency and accountability.
Ensure organizational compliance with data governance policies, standards, and relevant regulatory requirements.

Data Analysis and Reporting:
Conduct in-depth data analysis to uncover trends, patterns, anomalies, and areas for quality improvement.
Define, implement, and maintain KPIs to monitor data quality, usability, and performance across domains.
Deliver actionable insights using statistical techniques, SQL, Excel, Power BI, and AI-powered data quality tools to support data-driven decisions.

Stakeholder Collaboration: 
Engage with business users, data owners, and IT teams to understand data requirements and resolve quality issues effectively.
Provide ongoing support and guidance on data quality best practices to ensure consistent understanding and adoption.
Partner with Global Data Owners   to define and agree on P1/P2/P3 KPIs for their respective data products.
Monitor KPI status, proactively flag amber/red indicators, and initiate timely analysis and remediation efforts.

Continuous Improvement:
Continuously enhance personal knowledge and skills in data quality management through training, certifications, and staying updated with industry advancements, contributing to ongoing improvement efforts.