Senior Manager, Data & Analytics
New
J
JobgetherLife sciences
Based in the United StatesFull-TimeManager
Salary not disclosed
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Job Details
- Experience
- Doctorate degree plus 2 years of relevant experience; or Master’s degree plus 6 years; or Bachelor’s degree plus 8 years; or Associate’s degree plus 10 years; or high school diploma/GED plus 12 years of experience in Data Engineering, Information Systems, Business, Engineering, Data & Analytics, or a related field. At least 2 years of direct people management and/or leadership experience managing teams, projects, programs, or resource allocation; this experience may overlap with the required technical experience.
- Required Skills
- AWSAgileMicrosoft Power BIData engineeringDatabricksData analytics
Requirements
- Hold a doctorate plus 2 years, master’s degree plus 6 years, bachelor’s degree plus 8 years, associate’s degree plus 10 years, or high school diploma/GED plus 12 years of relevant experience in a listed or related field.
- Have at least 2 years of direct people management and/or leadership experience managing teams, projects, programs, or resource allocation; this may overlap with technical experience.
- Have experience delivering data, analytics, reporting, or digital solutions in the life sciences industry and working with large, globally distributed teams.
- Have experience leading cross-functional data and analytics initiatives from discovery through production delivery, adoption, and ongoing support.
- Have hands-on experience delivering solutions using Databricks, AWS, Power BI, and related data, analytics, cloud, integration, or visualization technologies.
- Translate business requirements into product specifications, delivery plans, measurable outcomes, and production-ready solutions.
- Have stakeholder management, communication, facilitation, and decision-making capabilities.
- Have experience with Agile or scaled Agile methodologies, including roadmaps, backlog management, PI planning, release planning, and continuous improvement.
- Have experience developing reusable enterprise data products such as governed datasets, data pipelines, semantic models, APIs, or analytics solutions.
- Be familiar with AI-enabled or agentic data-engineering capabilities, including pipeline automation, data-quality remediation, metadata generation, observability, and AI-assisted engineering workflows.
- Preferred: experience leading enterprise-scale data, analytics, reporting, AI, or digital-product initiatives in complex, matrixed organizations.
- Preferred: knowledge of GxP, data validation, data governance, privacy, security, and regulatory expectations for life sciences data and analytics.
Responsibilities
- Lead discovery sessions and translate business priorities into product visions, value cases, roadmaps, backlogs, and success criteria.
- Align stakeholders on priorities, scope, investment, sequencing, risks, dependencies, and delivery trade-offs.
- Communicate strategy, delivery progress, decisions, risks, and business value to technical and business audiences.
- Lead data and analytics initiatives from discovery through solution design, development, testing, production release, adoption, and ongoing support.
- Identify, evaluate, and scale data, analytics, reporting, and AI-enabled capabilities.
- Lead delivery of reusable enterprise data products with governed, discoverable, high-quality, and scalable data access.
- Partner with engineering teams on agentic data-engineering capabilities, including pipeline development, data-quality monitoring, metadata generation, observability, and issue resolution.
- Balance innovation with architecture, security, privacy, compliance, reliability, cost management, and operational-support requirements.
- Define and monitor adoption, operational, delivery, and business-value metrics; lead post-launch adoption and lifecycle management.
- Coordinate stakeholders, promote reuse of enterprise platforms and shared data products, and support Agile delivery and continuous improvement.
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