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# Data Quality Specialist

- **Company:** Vopak
- **Location:** Rotterdam, Netherlands
- **Experience:** Experienced
- **Salary:** €5,417.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Data Governance, Machine Learning, Metadata Repositories, Standard Sql, SQL Databases, Enterprise Data Management, Information Technology, Data Pipelines
- **Published:** June 6, 2026
- **Apply:** https://nl.indeed.com/viewjob?jk=6505498fb6c72687

## About the Role

Do you have experience in SQL?, Do you have a Master's degree?, * You have a Master's Degree professional and intellectual level.

 * You have 3+ years of relevant work experience in an Enterprise Data Quality Specialist role.
 * You are proficient in both Dutch and English.
 * You have strong technical experience with Data Governance, Data Quality Tooling, Data Catalogs, data modeling, business rules, and working SQL knowledge.
 * You possess demonstrated strong analytical and problem-solving skills, coupled with excellent stakeholder management abilities to coordinate improvements across complex global teams.

Don't meet all our criteria?

No problem! If this role energizes you and our culture feels like a good fit, we warmly invite you to apply anyway.

## Description

As the Data Quality Specialist, your mission is to optimize, expand, and create practices that improve the quality of data across our entire IT/OT landscape. By ensuring our data is reliable and well-governed, you will directly support our North Star of becoming an "Efficient IT Operator," enabling IT to be "Better, Faster, and Cheaper". Operating within the newly formed Corporate Information Office, you will ensure vital "data readiness" for new AI and analytics opportunities, acting on the principle of applying "Data and AI with value discipline"., * Maintaining and Improving Frameworks: Maintaining the Data Quality (metrics) framework and designing Data Quality Dashboards to proactively discover bad quality data across APIs, pipelines, and AI/ML models.

 * Ensuring Data Readiness: Participating in business and IT projects to determine data quality requirements and ensure data readiness checks are completed for new AI and analytics opportunities before they are piloted or scaled .
 * Collaborating for Improvement: Working together with business stakeholders, data owners, system architects, and Data/ML Engineers to improve data pipelines, ML algorithms, and overall data quality management.
 * Community and Governance: Establishing and supporting a community responsible for data quality, providing consulting services, and facilitating the DQ incident and problem process to solve complex data quality issues.


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