APAC Data Quality Governance Manager

About Johnson & Johnson Innovative Medicine

Johnson & Johnson is dedicated to advancing health by creating smarter, less invasive treatments and personalized healthcare solutions. By leveraging expertise in Innovative Medicine and MedTech, the company aims to prevent, treat, and cure complex diseases globally.

Role Summary

The APAC Data Quality Governance Manager is responsible for enhancing the reliability, usability, and accountability of data products and knowledge assets within the Asia-Pacific commercial team, covering key markets such as Japan, Australia, Korea, and China. This governance-focused, managerial level position involves coordinating with IT and delivery partners to implement data quality controls while acting as the expert and liaison in cross-functional committees and regional-global forums. The role requires strong stakeholder engagement and influence without direct authority.

Key Responsibilities

• Develop and enforce the APAC-specific data quality governance framework, including standards, roles, and operational rhythms.
• Manage a data quality roadmap prioritizing critical data products, improvement initiatives, and adoption of enterprise-wide standards.
• Advocate the integration of quality principles throughout the entire data product lifecycle with owners and producers.
• Set clear, fit-for-purpose quality expectations for critical data products based on varied business and AI-enabled use cases.
• Oversee data quality issue governance from detection to resolution and prevention of recurrence.
• Monitor performance through meaningful KPIs and maintain transparency with quality scoring for key data assets.
• Align data governance practices across APAC markets and represent regional views in global data quality discussions.
• Address governance for both structured and unstructured data assets used in analytics, machine learning, and generative AI applications.
• Facilitate cooperation between business stakeholders and IT teams translating business needs into technical data quality requirements.
• Coordinate delivery efforts with contractors and partners, ensuring clear work packages and adherence to acceptance criteria.
• Influence diverse stakeholders by connecting data quality improvements to business risk, outcomes, and patient impact.
• Design and implement data quality KPIs, report trends and issues, and drive root-cause analysis to encourage preventive measures.

Candidate Profile

• Extensive experience in data quality, governance, data management, or analytics, with a track record of developing sustainable data controls aligned to business needs.
• Deep knowledge of data products, metadata, data lineage, transformations, and consumption patterns; capable of engaging technical teams confidently.
• Expertise in defining quality expectations for diverse data types including structured, semi-structured, and unstructured content.
• Proven ability to lead governance initiatives, resolve ambiguities, and influence without direct authority in a multicultural and remote environment.
• Fluent English communication skills; proficiency in Japanese, Korean, or Chinese is advantageous.

Preferred Experience

• Work background within regulated sectors such as healthcare, pharmaceuticals, or life sciences.
• Adaptation of regional data governance standards across diverse APAC markets.
• Experience evaluating external data fitness during acquisition and onboarding phases.
• Working knowledge of data quality tools, metadata/catalog platforms, cloud environments, SQL, and monitoring solutions tailored for generative AI content.
• Familiarity managing IT delivery teams, outsourced partners, and specialist contractors.
• Building data stewardship communities and fostering adoption via influence rather than authority.

Work Culture & Success Criteria

• Establish shared, clear definitions of data quality across stakeholders.
• Embed quality by design across data lifecycle phases.
• Promote trustworthy and measurable data products with ownership and governance clarity.
• Maintain transparency on data quality performance via KPIs and scoring.
• Ensure readiness of data assets for generative AI with maintained trustworthiness and compliance.
• Increase consumer trust in data for decision-making, analytics, and AI usage.
• Drive IT delivery alignment and timely remediation efforts.
• Encourage sustainable improvements focusing on preventive controls rather than reactive fixes.
• Positively impact patient outcomes through high-quality data supporting commercial and healthcare decisions.