APAC Data Quality Governance Manager

At Johnson & Johnsonwe believe health is everything. Our strength in healthcare innovation empowers us to build aworld where complex diseases are prevented treated and curedwhere treatments are smarter and less invasive andsolutions are our expertise in Innovative Medicine and MedTech we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow and profoundly impact health for more at .

As guided by Our Credo Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson & Johnson we respect the diversity and dignity of our employees and recognize their merit.

Job Function:

Job Sub Function:

Job Category:

All Job Posting Locations:

Job Description:

Johnson & Johnson Innovative Medicine is seeking a Data Quality Governance Manager to strengthen the trust usability and accountability of APAC Data Products and knowledge assets. The position sits within the APAC Commercial Team with Japan Australia Korea and China as the main markets while supporting the wider region. Data Quality is the primary focus of the role. The position is at Manager level and currently has no direct reports. It works through influence and coordinates contractors and delivery partners.

This is a governance-led and stakeholder-facing role not a hands-on engineering position. The role translates business and Data Product needs into clear Data Quality requirements and controls while coordinating and delegating technical implementation monitoring and remediation activities to IT and other delivery partners.

Success depends on influence without authority. The role serves as the Data Quality expert in the cross-functional Data Governance Committee and represents APAC in global Data Quality forums. It connects business teams Data Product Owners data owners data producers data consumers data stewards and technology teams to create shared accountability.

• APAC Data Quality governance framework principles standards roles decision rights and operating rhythm.
• APAC Data Quality roadmap including prioritization of critical Data Products and data domains improvement initiatives capability development tooling evolution and adoption of enterprise standards.
• Promotion of Quality by Design with Data Product Owners data owners and data producers so quality expectations controls and accountability are embedded throughout the Data Product lifecycle.
• Definition of fit-for-purpose Data Quality expectations for critical Data Products and knowledge assets based on their business purpose consumers reporting analytics and AI-enabled use cases.
• Prioritization of critical Data Products data elements quality risks and improvements based on consumer needs business impact and intended use.
• Data Quality issue governance from identification and triage through ownership remediation escalation closure and prevention of recurrence.
• Performance monitoring through meaningful Data Quality KPIs including a transparent DQ score for priority Data Products.
• Regional alignment across the main markets of Japan Australia Korea and China and representation of APAC in global Data Quality forums.
• Data Quality governance for structured semi-structured and unstructured Data Products and knowledge assets used by analytics Machine Learning Generative AI and Copilot-enabled experiences across APAC.

• Develop and maintain a practical APAC Data Quality framework aligned with enterprise policies and regional business needs.
• Partner with Data Product Owners data owners data producers data stewards and consumers to define quality rules acceptance criteria thresholds and preventive controls.
• Promote Quality by Design throughout the Data Product lifecycle from design and acquisition through development deployment operation change and retirement rather than relying only on downstream detection and correction.
• Clarify ownership and decision rights across Data Product Owners data producers consumers stewards platform teams and market stakeholders.
• Act as the Data Quality expert in the cross-functional Data Governance Committee and facilitate decisions escalation and follow-through.
• Represent APAC priorities perspectives performance and lessons learned in global Data Quality forums.

• Build a detailed understanding of priority Data Products including their business purpose consumers lineage transformations metadata quality requirements and downstream consumption.
• Assess quality in relation to the intended use of each Data Product distinguishing technically detectable issues from those requiring business or consumer judgment.
• Translate business impact into precise testable requirements that technology teams can implement.
• Establish Data Quality assessment criteria for external data providers and contribute to vendor evaluation data acquisition onboarding and periodic quality reviews including assessment of fitness for purpose limitations and risks.

• Address Data Quality needs for unstructured and semi-structured content used by Generative AI including documents text presentations images and other knowledge assets.
• Establish fit-for-purpose quality expectations for GenAI content covering accuracy completeness currency consistency provenance metadata accessibility discoverability and appropriate access.
• Partner with Data Product Owners knowledge owners business teams and technology teams to ensure content used by Generative AI and Copilot-enabled experiences is trusted traceable governed and maintained.
• Define quality indicators for AI-ready Data Products and knowledge assets including measures that support effective discovery and retrieval while remaining aligned with business use and risk.

• Act as the bridge between business stakeholders and IT teams translating business objectives into actionable technical requirements and ensuring technical solutions meet business Data Quality expectations.
• Connect Data Product Owners consumers producers stewards and technology teams so that quality decisions reflect consumer needs business value and technical feasibility.

• Create clear work packages for IT and delivery partners covering monitoring root-cause analysis technical controls automation and remediation.
• Provide functional direction and acceptance criteria while maintaining separation between governance accountability and technical execution.
• Monitor delivery resolve ambiguity manage dependencies and escalate when progress or risk requires leadership attention.
• Coordinate contractors and specialist resources setting clear deliverables monitoring progress and ensuring outputs meet defined standards. The role currently has no direct reports.

• Influence stakeholders without formal authority by connecting Data Quality to business outcomes risk user experience and patient impact.
• Create alignment across countries functions and technical teams with different priorities and levels of data maturity.
• Communicate complex data topics clearly for executives data practitioners business users and technology teams.
• Build capability through coaching guidance reusable standards and practical examples.

• Establish Data Quality KPIs and a DQ score that provide a transparent view of quality performance for priority Data Products including indicators relevant to structured and unstructured content.
• Report quality trends open issues business impact ownership remediation status and recurring patterns.
• Use root-cause insights to move the organization from reactive correction toward prevention and Quality by Design.
• Review controls and KPIs as data sources business processes products and analytical use cases evolve.

Shared definition

• Business and technology teams use clear consistent definitions of acceptable quality for priority Data Products and knowledge assets.

Quality by Design

• Data Product Owners data owners and producers embed quality requirements and preventive controls throughout the Data Product lifecycle.

Trusted Data Products

• Critical Data Products have defined owners consumers quality requirements governance processes and measurable quality outcomes.

Measurable performance

• DQ scores and supporting KPIs make performance trends and priorities transparent.

GenAI readiness

• Structured and unstructured Data Products and knowledge assets are trusted discoverable traceable current and fit for approved Generative AI use cases.

Consumer trust

• Data consumers understand Data Product quality levels and limitations and have greater confidence using data for decisions analytics and AI.

Effective delivery

• IT receives clear requirements and delivers monitoring and remediation against agreed acceptance criteria.

Sustainable improvement

• Recurring problems are addressed at root cause and preventive controls become part of normal operations.

Patient impact

• Trusted data supports better commercial decisions and healthcare activities that contribute to improving patients lives.

• Experience in Data Quality Data Governance Data Management analytics or a related discipline with evidence of translating business needs into sustainable data controls.
• Strong understanding of Data Products data structures databases pipelines transformations lineage metadata and analytical consumption. Coding is not required but confidence engaging technical teams is essential.
• Ability to determine what good quality means for structured semi-structured and unstructured Data Products by understanding content source processing intended use metadata and consumer expectations.
• Proven ability to influence decisions and drive action where there is no direct reporting authority.
• Experience facilitating cross-functional governance negotiating ownership resolving ambiguity and escalating constructively.
• Excellent communication and stakeholder-management skills in a remote multicultural multi-country environment.
• Fluency in English.

• Experience in healthcare pharmaceuticals life sciences or another regulated environment.
• Experience working across APAC markets and adapting regional standards to local contexts.
• Experience evaluating external data quality and fitness for purpose during data acquisition or onboarding.
• Familiarity with Data Quality platforms metadata or catalog tools cloud data platforms SQL automated monitoring and quality considerations for unstructured content used by Generative AI.
• Experience working with IT delivery teams managed services contractors or external partners.
• Experience building or enabling Data Stewardship communities and driving adoption through influence rather than formal authority.
• Working proficiency in Japanese Korean or Chinese in addition to English.

• Business-minded Data Product professional: You connect Data Product quality to consumer needs decisions outcomes and trust not only to technical defects.
• Quality by Design advocate: You help data owners and producers prevent quality issues at source.
• Governance builder: You create clarity around standards roles decisions and accountability without making governance unnecessarily heavy.
• Influencer without authority: You earn trust frame issues objectively navigate competing priorities and help stakeholders commit to action.
• Pragmatic orchestrator: You know what must be governed what should be delegated and how to hold delivery partners accountable.
• Regional and global collaborator: You work effectively across cultures and markets and can represent APAC in global forums.
• Manager-level individual contributor: You operate with managerial scope and accountability coordinate contractors and deliver through influence without relying on direct reports.

• Shape how trusted Data Products and knowledge assets enable better decisions across a diverse and strategically important region.
• Help improve patients lives by strengthening the quality and trustworthiness of Data Products used to inform commercial decisions and healthcare activities.
• Create sustainable Data Quality capability by connecting business accountability with strong technology execution.
• Work at the intersection of data governance analytics technology and business transformation.

The essence of the role

Understand how Data Products are used. Build quality in by design. Govern structured and unstructured content. Measure transparently. Enable trusted analytics and Generative AI.

Required Skills:

Preferred Skills: