Data Management Associate Director

Job Description

ABOUT AMGEN

Amgen harnesses the best of biology and technology to fight the world’s toughest diseases and make people’s lives easier, fuller, and longer. We discover, develop, manufacture, and deliver innovative medicines to help millions of patients. Amgen helped establish the biotechnology industry more than 40 years ago and remains on the cutting edge of innovation, using technology and human genetic data to push beyond what’s known today.

ABOUT THE ROLE

Role Description

The Associate Director, Data Management will partner closely with Data Management Leads to manage and advance key commercial data management responsibilities from the Amgen Innovation Center in India. This role will support the delivery of reliable, well-governed, and analytics-ready commercial data by collaborating across AIN, Decision Sciences, Value & Access, BAI, DTI, and AI&D teams.

This role is also expected to lead a horizontal capability across Data Platforms, Patient Data Management, and Master Data Management. The Associate Director will focus on driving innovation and operational excellence initiatives across these areas, identifying opportunities to improve processes, strengthen data quality, increase automation, and enhance scalability across commercial data operations.

The Associate Director will play a critical role in enabling scalable data quality monitoring, supporting enterprise data governance solutions, strengthening metadata and lineage practices, improving data standards, and leveraging emerging technologies, including AI, to enhance productivity, quality, and operational efficiency.

This role requires strong commercial data management expertise, cross-functional collaboration skills, and the ability to operate effectively across global teams and time zones.

Roles & Responsibilities

Strong Partnership with Data Management Leads

Partner closely with Data Management Leads to manage commercial data management responsibilities across data quality, governance, metadata, access reviews, documentation, and analytics enablement.

Support execution of priority initiatives and ensure alignment with US teams on goals, standards, timelines, and expected outcomes.

Act as a key liaison between AIN-based teams and US stakeholders to ensure smooth communication, issue resolution, and delivery continuity.

Lead Horizontal Capability Across Data Platforms, PDM, and MDM

Lead a horizontal capability across Data Platforms, Patient Data Management, and Master Data Management, with a focus on driving innovation, operational excellence, and scalable ways of working.

Identify and implement opportunities to improve efficiency, standardization, automation, and quality across these areas.

Partner with pillar leads and cross-functional stakeholders to align on shared priorities, common standards, reusable frameworks, and continuous improvement opportunities.

Drive initiatives that improve operational consistency, reduce manual effort, strengthen issue resolution processes, and enhance overall data management maturity.

Promote adoption of innovative tools, AI-enabled solutions, and best practices to improve productivity and business impact across Data Platforms, PDM, and MDM.

Enable Reliable Commercial Data for Analytics and Business Decisions

Collaborate with cross-functional teams across AIN, Decision Sciences, Value & Access, BAI, DTI, and AI&D to deliver reliable commercial data for analytics, reporting, and business decision-making.

Support the identification, investigation, and resolution of commercial data issues in partnership with relevant business, analytics, and technology stakeholders.

Drive consistency and quality across commercial data deliverables to enable trusted downstream consumption.

Scale Commercial Data Quality Monitoring

Partner closely with US teams to enable, scale, and manage the commercial data quality dashboard for ongoing data monitoring.

Leverage tools such as Databricks Genie, ChatGPT, GitLab Duo, and other emerging technologies to identify data issues, detect trends, streamline analysis, improve documentation, and enhance dashboard functionality.

Support root-cause analysis for recurring data issues and partner with stakeholders to drive timely remediation and continuous improvement.

Support Enterprise Data Management and Governance Solutions

Collaborate with AI&D and DTI stakeholders to develop, implement, and refine enterprise data management solutions, including Collibra, DMP, and FAIR-aligned practices.

Support metadata management, data cataloguing, governance workflows, lineage documentation, and the establishment of reusable data standards.

Help enhance data discoverability, transparency, governance adoption, and consistency across the enterprise.

Leverage AI and Emerging Technologies

Use emerging technologies, including AI, to make data analytics and data management processes more efficient.

Automate routine tasks, review code, improve documentation, accelerate root-cause analysis, strengthen data quality checks, and enhance team productivity.

Champion practical adoption of AI-enabled tools and workflows while ensuring alignment with data governance, quality, privacy, and compliance expectations.

Conduct Data Access Reviews and Support Audit Readiness

Conduct quarterly data access reviews to validate appropriate access across commercial data assets and platforms.

Identify remediation needs, support audit readiness, and ensure continued alignment with privacy, compliance, and least-privilege access principles.

Partner with business, technology, and governance stakeholders to document findings and track remediation actions to closure.

Standardize and Maintain Key Commercial Data Deliverables

Standardize, document, and maintain key commercial data deliverables, including source data layouts, reference data structures, data dictionaries, reporting solutions, and downstream consumption requirements.

Promote consistency across deliverables by documenting standards, clarifying ownership, and enabling scalable reuse across analytics and reporting use cases.

Ensure commercial data documentation remains current, accessible, and aligned with business and technical requirements.

Drive Cross-Functional Alignment and Continuous Improvement

Build and sustain strong working relationships across cross-functional teams in AIN.

Drive alignment on commercial data strategy, governance expectations, analytics priorities, and delivery timelines.

Facilitate effective communication, knowledge sharing, and cross-team problem solving to support high-quality outcomes and continuous improvement.

Identify process gaps, operational risks, and improvement opportunities, and partner with stakeholders to implement scalable solutions.

Basic Qualifications and Experience

Any degree and 16 to 20 years of relevant data management experience

Experience in commercial data management, data governance, data quality, metadata management, or analytics enablement.

Experience working across one or more commercial data domains such as Data Platforms, Patient Data Management, Master Data Management, Data Acquisition, or Data Governance.

Experience working with global teams across geographies, time zones, and functions.

Strong stakeholder management, communication, and problem-solving skills.

Excellent English oral and written communication skills.

Comfortable operating in a matrixed organization and partnering across business, analytics, technology, and governance teams.

Functional Skills

Must-Have Skills

Strong understanding of commercial data management concepts, including data quality, metadata, data lineage, data governance, and data documentation.

Experience working across Data Platforms, Patient Data Management, Master Data Management, or related commercial data capabilities.

Ability to drive horizontal initiatives focused on innovation, operational excellence, automation, standardization, and scalable delivery.

Ability to partner with business and technology stakeholders to translate data issues into clear actions, ownership, and remediation plans.

Experience standardizing data deliverables such as source layouts, data dictionaries, reference data structures, reporting requirements, and downstream consumption documentation.

Ability to leverage emerging technologies, including AI-enabled tools, to improve productivity, documentation, analysis, code review, and root-cause analysis.

Ability to drive continuous improvement, identify operational gaps, and implement scalable solutions.

Good-to-Have Skills

Experience in the life sciences commercial data domain.

Familiarity with enterprise data management platforms and practices such as Collibra, DMP, FAIR principles, Databricks, GitLab Duo, and AI-enabled analytics tools.

Exposure to commercial analytics, reporting, data platforms, master data management, patient data, or data acquisition and governance.

Experience supporting data access reviews and least-privilege access principles.

Familiarity with Agile ways of working or Scaled Agile Framework principles.

Experience working in multinational environments with globally distributed teams.

Soft Skills

Excellent collaboration and stakeholder management skills.

Strong verbal, written, and presentation skills, with the ability to communicate complex data concepts clearly and effectively.

Strong analytical thinking and problem-solving abilities.

Ability to manage multiple priorities in a fast-paced and evolving environment.

High attention to detail and commitment to data quality and operational excellence.

Ability to influence without authority and drive alignment across cross-functional teams.

Strong ownership mindset with a focus on accountability, continuous improvement, and business impact.