Senior Manager, Data & Analytics

United StatesRemoteFull-timesenior

OtherData & Analytics

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Quick Summary

Overview

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Manager, Data & Analytics based in the United States.

Technical Tools

OtherData & Analytics

This role serves as a strategic business engagement and technical delivery leader for enterprise data and analytics initiatives.
You’ll translate business priorities into scalable data, reporting, analytics, and AI-enabled solutions that deliver measurable value.
The position spans the full lifecycle, from discovery and product strategy through delivery, adoption, and continuous enhancement.
You’ll collaborate with business owners, engineering, architecture, governance, Agile delivery, and global technical teams.
The role combines hands-on technical understanding with strong product leadership, stakeholder management, and business acumen.
You’ll work with platforms such as Databricks, AWS, Power BI, and emerging AI-enabled data engineering capabilities.
This is an opportunity to shape enterprise data products and help transform how complex organizations use data to make decisions.

Lead discovery sessions with business owners and translate priorities into product visions, value cases, roadmaps, backlogs, and measurable success criteria.
Maintain alignment across stakeholders on priorities, scope, investment, sequencing, risks, dependencies, and delivery trade-offs.
Communicate strategy, delivery progress, key decisions, risks, and realized business value to technical and business audiences.
Lead cross-functional data and analytics initiatives from discovery through solution design, development, testing, production release, adoption, and ongoing support.
Identify, evaluate, and scale innovative data, analytics, reporting, and AI-enabled capabilities that improve decision-making, productivity, automation, and business outcomes.
Lead the delivery of reusable enterprise data products that provide governed, discoverable, high-quality, and scalable access to data.
Partner with engineering teams to evaluate and apply agentic data-engineering capabilities, including automated pipeline development, data-quality monitoring, metadata generation, observability, and issue resolution.
Balance innovation with enterprise requirements for architecture, security, privacy, compliance, reliability, cost management, and operational support.
Define and monitor adoption, operational, delivery, and business-value metrics for assigned initiatives.
Lead post-launch adoption, support, enhancement prioritization, and lifecycle management.
Promote reuse of enterprise platforms, shared data products, APIs, semantic models, and engineering patterns to enable scalable delivery.
Coordinate across business, engineering, architecture, governance, and program-management stakeholders to resolve dependencies and drive decisions to completion.
Support continuous improvement through Agile delivery practices, prioritization, roadmap management, and effective resource allocation.

Requirements

~2 min read

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.
Demonstrated experience delivering data, analytics, reporting, or digital solutions within the life sciences industry and working with large, globally distributed teams.
Proven experience leading cross-functional data and analytics initiatives from discovery through production delivery, adoption, and ongoing support.
Hands-on experience delivering solutions using Databricks, AWS, Power BI, and related data, analytics, cloud, integration, or visualization technologies.
Ability to translate business requirements into product specifications, delivery plans, measurable outcomes, and production-ready solutions.
Strong stakeholder management, communication, facilitation, and decision-making capabilities.
Experience leading enterprise-scale data, analytics, reporting, AI, or digital-product initiatives within complex, matrixed organizations is preferred.
Experience with Agile or scaled Agile methodologies, including product roadmaps, backlog management, PI planning, release planning, and continuous improvement.
Experience developing reusable enterprise data products such as governed datasets, data pipelines, semantic models, APIs, or analytics solutions.
Familiarity with AI-enabled or agentic data-engineering capabilities, including intelligent pipeline automation, data-quality remediation, metadata generation, observability, and AI-assisted engineering workflows.
Knowledge of GxP, data validation, data governance, privacy, security, and regulatory expectations applicable to life sciences data and analytics is preferred.
Strong ability to manage competing priorities, navigate ambiguity, evaluate trade-offs, and drive decisions through completion.
Strong strategic thinking, technical curiosity, business acumen, and ability to operate effectively across technical and non-technical environments.