Description:

About the Role We are seeking a highly accomplished Data Quality Manager with a strong research background in Data (not Data Science).

The ideal candidate will hold a PhD and demonstrate a proven academic record with published research papers in IEEE Q1 or Q2 journals.

Candidates must also bring practical, hands-on experience as a Data Architect, with a strong track record of identifying, resolving, and preventing complex data quality issues in enterprise environments.

This role blends academic excellence, applied research, and real-world data architecture leadership, and is suited for professionals who actively engage with IEEE reviewers and editorial communities.

Key Responsibilities:

- Data Quality Strategy & Leadership Define and lead enterprise-level data quality strategies, frameworks, and governance models.

- Establish data quality standards, metrics, KPIs, and continuous monitoring mechanisms.

- Identify root causes of data quality issues and implement scalable remediation solutions.

- Drive improvements in data accuracy, consistency, completeness, timeliness, and integrity.

- Data Architecture & Governance Design, review, and maintain enterprise data architectures that support high-quality data management.

- Embed data quality controls within data pipelines, platforms, and integration layers.

- Define and enforce data governance policies, metadata management, lineage, and master data practices.

- Collaborate with engineering and platform teams to ensure architecture aligns with quality objectives.

- Research & Academic Contribution Conduct advanced research in the data domain (explicitly excluding Data Science or ML-focused research).

- Publish research in IEEE Q1/Q2 journals and participate in high-impact conferences.

- Leverage established relationships with IEEE reviewers to maintain research rigor and relevance.

- Translate academic findings into actionable enterprise data quality solutions and best practices.

- Standards, Compliance & Review Align data quality practices with industry standards, regulatory requirements, and compliance frameworks.

- Review and assess internal and external data quality methodologies and research outputs.

- Contribute to technical whitepapers, standards documentation, and internal guidelines.

- Cross-Functional Collaboration & Mentorship Act as a subject-matter expert (SME) for data quality and data architecture initiatives.

- Collaborate with cross-functional teams including engineering, product, governance, and leadership.

- Mentor internal teams on data quality principles, architectural best practices, and research methodologies.

Required Qualifications:

- PhD in Data, Information Systems, Computer Science, or a closely related field (Data Sciencefocused PhDs will not be considered)

- 812 years of overall experience, combining academic research and hands-on industry experience in data architecture and data quality management.

- Published research papers in IEEE Q1 or Q2 journals (mandatory; other journals will not be accepted).

- Demonstrated prior working relationship or collaboration with IEEE reviewers or editorial boards.

- Proven hands-on experience as a Data Architect, addressing complex data quality challenges.

- Strong expertise in data quality frameworks, governance models, and remediation techniques.

- Ability to bridge academic research and enterprise data architecture implementation.

Preferred / Added Qualifications:

- Participation in IEEE working groups, editorial committees, or standards bodies.

- Experience defining or contributing to data quality standards or frameworks.

- Strong publication record with measurable impact (citations, impact factor, h-index).

- Experience authoring technical whitepapers, patents, or standards documentation