Senior Manager, Data Enablement - Obesity

Career Category Engineering Job Description Senior Manager, Data Enablement

Obesity Intelligence & Analytics | Amgen India Reports to: Director, Technology, Operations & Data Enablement Role Summary This is an India-based global role supporting Amgen's global obesity business. The Senior Manager, Data Enablement will own the data foundation that enables analytics, products, AI, and decision-making to move at the speed of the business. The mission is simple: analytical teams should spend their time solving business problems - not searching for data, reconciling inconsistencies, or rebuilding datasets for every new question. This is not a traditional data management role. It requires technical depth, business judgment, and an appreciation for how high-quality, well-governed data accelerates decisions across the organization. This leader will own Obesity data readiness. In close partnership with enterprise data and technology teams, the role will ensure the data ecosystem meets business needs by making datasets business-ready and AI-ready, while influencing priorities so analytics can move at the pace the business requires. Success depends on balancing speed with governance, aligning data with enterprise architecture, privacy, security, and quality standards, and keeping it fit for rapidly evolving business needs. Success is measured by how quickly new questions can be answered, how confidently teams trust the data they use, and how effectively the organization can scale analytics, AI-enabled solutions, and future products without data becoming the constraint. Key Responsibilities

1. Data Readiness Agenda & Use-Case Prioritization Own the data readiness agenda supporting priority obesity analytics, products, AI/ML, reporting, and decision-support use cases. Partner with analytics, product, commercial, scientific, operations, and technology teams to understand data needs before analytical work begins. Convert ambiguous requests into clear data requirements, source plans, readiness criteria, quality expectations, and delivery commitments. Prioritize data work based on business value, reuse potential, urgency, governance requirements, quality risk, and downstream analytical impact.
2. Data Onboarding, Integration & Business-Ready Preparation Lead onboarding of internal and external sources required for analytics products, business intelligence, AI/ML, and future decision-support tools. Coordinate sourcing, access, mapping, integration, transformation, testing, and handoff so datasets can be used reliably by downstream teams. Work with enterprise data and engineering partners to align pipelines, curated layers, refresh patterns, and access models with platform direction. Resolve practical data issues that slow teams down, including mismatched definitions, missing fields, unclear ownership, inconsistent granularity, and usability gaps.
3. Data Quality, Controls & Issue Resolution Establish quality checks, validation rules, issue logs, remediation processes, escalation paths, and sign-off expectations for priority data assets. Identify recurring defects, root causes, control gaps, and ownership breakdowns, then drive fixes that prevent the same problems from returning. Create practical standards for reconciliation, refresh monitoring, exception handling, data stability, and known limitation management. Partner with source owners, technology teams, vendors, and business users to resolve problems quickly, transparently, and with clear accountability. Protect analytical credibility. If the data is weak, unclear, late, or unstable, teams should know before insights, products, or models depend on it.
4. Reusable Data Products, Documentation & AI-Ready Assets Build reusable data products, semantic layers, standardized business rules, and documentation that improve consistency and reduce repeated manual work. Define and maintain metadata, lineage, data dictionaries, transformation logic, ownership, refresh expectations, known limitations, and usage guidance. Enable self-service where appropriate, while keeping controls strong enough for regulated healthcare data and sensitive business information. Make reuse easy. Every high-value dataset should become easier for the next team to understand, trust, apply, and extend.
5. Governance, Compliance & Enterprise Partnership Partner with privacy, security, legal, compliance, architecture, and enterprise data teams to ensure responsible data use. Translate governance requirements into practical ways of working that teams can follow without unnecessary friction or delay. Support appropriate access management, documentation, retention expectations, vendor coordination, data-use controls, and audit readiness. Represent obesity intelligence data needs clearly with enterprise technology partners, bridging gaps between policy, platform direction, and analytical reality.
6. Team Leadership, Service Model & Capability Building Lead an India-based data enablement team that provides disciplined, responsive support to analytics, products, and AI/ML use cases. Create a service model for intake, triage, delivery, documentation, quality checks, issue resolution, stakeholder communication, and ongoing support. Coach team members to combine technical rigor with business understanding, customer-service orientation, and ownership of downstream impact. Set clear expectations for responsiveness, documentation, reuse, quality, transparency, and practical problem solving. Basic Qualifications Doctorate degree and 4+ years of relevant experience; OR Master's degree and 8+ years of relevant experience; OR Bachelor's degree and 10+ years of relevant experience. Preferred Qualifications Advanced degree in Data Science, Computer Science, Engineering, Information Systems, Analytics, Business, Life Sciences, or a related field. Demonstrated experience leading data enablement, analytics engineering, or data platform initiatives that made trusted, business-ready data available for analytics, AI/ML, reporting, or decision-support. Demonstrated success designing and scaling reusable, governed data assets that improved analytical speed, consistency, data quality, or organizational reuse. Strong understanding of modern data management practices, including metadata, lineage, semantic layers, data quality, governance, access controls, and reusable data architecture. Experience building reusable data products, curated datasets, semantic layers, or self-service capabilities that reduced manual effort and improved analytical scalability. Experience working with healthcare, commercial, medical, operational, real-world, or external data sources supporting business analytics or decision-making. Experience influencing enterprise data engineering, architecture, privacy, security, and business stakeholders to align platform capabilities with business priorities. Understanding of data requirements for AI/ML applications, including data readiness, documentation, governance, and model enablement. Demonstrated ability to diagnose complex data issues, evaluate trade-offs, and drive practical, scalable solutions without creating unnecessary process or bureaucracy. Excellent communication skills, with the ability to explain complex data concepts, governance decisions, and technical trade-offs to both technical teams and business leaders.

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