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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Manager, Data & Analytics - **Company:** Amgen - **Location:** Washington, DC, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Agile Methodology, Artificial Intelligence, Amazon Web Services, Business Analytics Applications, Data Analysis, Information Systems, Information Engineering, Data Governance, Data Sharing, Release Management, Power BI, DataOps, Enterprise Data Management, Delivery Pipeline, Data Strategy, Data Analytics, Data Pipelines, GXP, Databricks - **Published:** October 3, 2026 - **Apply:** https://dejobs.org/x/x/855ADD94DD6A493F8E7EDF83133D1750/job/ ## About the Role Doctorate degree and 2 years of experience in Data Engineering, Information Systems, Business, Engineering, Data & Analytics, or a related field, Master's degree and 6 years of experience in Data Engineering, Information Systems, Business, Engineering, Data & Analytics, or a related field, Bachelor's degree and 8 years of experience in Data Engineering, Information Systems, Business, Engineering, Data & Analytics, or a related field, Associate's degree and 10 years of experience in Data Engineering, Information Systems, Business, Engineering, Data & Analytics, or a related field, High school diploma / GED and 12 years of experience in Data Engineering, Information Systems, Business, Engineering, Data & Analytics, or a related field In addition to meeting at least one of the above requirements, you must have a minimum of 2 years' experience directly managing people and/or leadership experience leading teams, projects, programs, or directing the allocation of resources. Your managerial experience may run concurrently with the required technical experience referenced above. Required Qualifications: · Experience delivering data, analytics, reporting, or digital solutions in the life sciences industry, working with large global teams · Demonstrated experience leading cross-functional data and analytics initiatives from discovery through production delivery, adoption, and ongoing support. · Demonstrated experience delivering solutions using Databricks, AWS, Power BI, and related data, analytics, cloud, integration, or visualization technologies. · Ability to translate business needs into product requirements, delivery plans, measurable outcomes, and production-ready solutions. · Strong stakeholder-management, communication, facilitation, and decision-making skills. Preferred Qualifications: · Experience leading enterprise-scale data, analytics, reporting, AI, or digital-product initiatives in a complex, matrixed organization. · Experience with Agile or scaled Agile delivery practices, including product roadmaps, backlog management, PI planning, release planning, and continuous improvement. · Experience developing or delivering reusable enterprise data products, including governed datasets, data pipelines, semantic models, APIs, or analytics products. · Experience with AI-enabled or agentic data-engineering capabilities, such as intelligent pipeline automation, data-quality remediation, metadata generation, data observability, or AI-assisted engineering workflows. · Knowledge of GxP, validation, data governance, privacy, security, and regulatory expectations relevant to life sciences data and analytics. · Ability to manage competing priorities, navigate ambiguity, make transparent trade-offs, and drive decisions to closure. ## Description Let's do this. Let's change the world. In this vital role, you will serve as the business engagement and technical delivery lead for prioritized Enterprise Data Strategy & Engineering (EDSE) initiatives. You will lead assigned initiatives from discovery through delivery, adoption, and value realization-translating business priorities into scalable data, analytics, reporting, and AI-enabled solutions. You will partner with Business Owners, EDSE Platform, Engineering, Governance, Architecture, Agile/PMO, and Amgen GCC Engineering teams to define outcomes, shape roadmaps and delivery plans, manage dependencies, and deliver measurable business value. Operating at the intersection of business strategy and technology, you will co-lead with Business Owners and Technical Product Owners/engineering leads to deliver incremental value through an outcome-based roadmap and prioritized backlog. The successful candidate will bring the technical depth to lead solutions using Databricks, AWS, Power BI, and other EDSE strategic platforms through solution design, build, testing, release, adoption, and ongoing enhancement. The ideal candidate is an outcome-oriented leader with life sciences experience, strong business acumen, and credible data and analytics delivery expertise. Responsibilities: Business engagement and product strategy · Lead discovery with Business Owners; translate priorities into product visions, value cases, roadmaps, backlogs, and measurable success criteria. · Maintain alignment on priorities, scope, investment, sequencing, risks, dependencies, and trade-offs. · Communicate strategy, delivery progress, key decisions, and realized value to business and technical stakeholders. Technical delivery leadership & innovation · Identify, evaluate, and scale innovative data, analytics, and AI-enabled capabilities that improve decision-making, productivity, automation, and business outcomes. · Lead the delivery of reusable data products that enable governed, discoverable, high-quality, and scalable use of enterprise data. · Partner with EDSE and AIN Engineering teams to evaluate and apply agentic data-engineering capabilities, including automated data pipeline development, data-quality monitoring, metadata and documentation generation, observability, and issue resolution, where appropriate. · Balance innovation with enterprise standards for architecture, security, privacy, compliance, reliability, cost, and operational support. Value realization and lifecycle management · Define and track adoption, operational, delivery, and business-value metrics for assigned initiatives. · Lead post-launch adoption, support, enhancement prioritization, and lifecycle management. · Promote reuse of EDSE platforms, shared data products, and engineering patterns to enable scalable enterprise delivery.