职位总体目的(主要职责)

You will play a critical role in promoting data governance within the whole organization. Reporting to the Data & Intelligence leader, you will be responsible for ensuring the data integrity, availability, accessability and usabilityof data across the organization. Your primary focus will be on driving data governance initiatives to establish data governance organization and guideline, setup data quality mechanism, define ownership and R&R, create change, resolving and  approval process, compile data definition and business glossary,  push for data process logic alignment and standardization etc. This position requires strong communication skills, collaboration mindset and technical expertise in data management, and a deep understanding of the pharmaceutical industry.

岗位职责：

"Establish Data Governance Framework:

Develop data governance policies, standards, and procedures to ensure data integrity, quality, and compliance with regulatory requirements.

Define data ownership, stewardship, and accountability frameworks to establish clear roles and responsibilities for managing data assets.

Implement Data Governance:

Establish data quality metrics, monitoring mechanisms, and remediation processes to address data inconsistencies and anomalies.

Compile metadata including business and technical data definition, business glossary, KPI definition,data lineage and data classification.

Build-up technical platform and map with DG framework and business requirement for systematic process and data flow management.

Organize  communication meeting for Data Governance Structure:

Regularly operate the communication of data governance meeting at both operational and management level,summarize the major progress ,issues and challenges, submit the hard-to-resolve dispute for arbitration, maintain the monthly exchange with Global CFDG for advice on successful experience, methodology and tools.

Key Stakeholder Engagement:

Act as a trusted DG ambassador  to promote the implementation of policy and SOP, ensure the clear data ownership definition and fulfillment of role and responsibility, monitor the approval process for data access, standardize data and KPI definition to eliminate inconsistency, receive DG related issue inquiry and step-in for conflict resolving.

Master data management:

Manage part of the master management domain(mainly HCP, speaker) including data sourcing, process execution, deliverable monitoring and contract renewing.

Assisting in BI & DW development:

Collaborate with BI and DW lead to track the adherence to SOP, create necessary requirement & definition document,determine the golden source for data ingestion, identify the owner for new reports and KPI logic, ensure the full compliance with policy and regulation and advice the access privilege & control etc.

Colaborate with business, compliance, IA and data security teams to periodically release the data quality status and improvement , report the data inconsistency findings ,check the data security & potential risk.

1.Bachelor’s or Master’s degree in Computer Science, Information Management, Life Sciences related fields.

2.At least 5 years’experience of data governance role in pharma industry.

3.Proficiency in written and spoken English. Japanese is a plus.

4.Expertise in data governance or data management framework(e.g., DAMA-DMBOK, CDMP), data quality, data lineage, meta data management and master data management (MDM).

5.Familiarity with pharma industry specific data categories and business domains such as HCO, HCP, DDI Data Flow,  Territory & Target, CPA/CPT, CHPA, CRM ,Event, Expense etc.

6.Familiarity with regulatory requirements and industry standards in the pharmaceutical sector, particularly in China.

7.Strong logic-thinking capability and high sense of potential risk and defect identifitication.

8.Excellent communication skills and collaboration mindset, able to develop persuading influce on key stakeholders.

9.Proficiency in SQL, data modeling and good-to-have BI tools capability (e.g., Tableau, Power BI).

10.Strong understanding of ETL (Extract, Transform, Load) processes and tools

11.Familiarity with  one or more mainstream data governance tools

(e.g., Collibra,Informatica,OpenMetaData）