Senior Data Product Manager

The Position

Why Genentech

We’re passionate about delivering on Our Promise to improve the lives of patients and create healthier communities for all. We foster a culture of inclusivity, integrity and creativity while boldly pursuing answers to the world’s most complex health challenges and transforming society.

Who We Are

Our Data, Analytics, and AI team is dedicated to solving complex healthcare challenges and improving patient outcomes. Data, Analytics, and AI empowers business partners across Commercial, Medical, and Government Affairs (CMG) to make impactful decisions by leveraging data, analytics, business products, and AI/ML to enable fast, targeted actions in rapidly evolving business contexts.

Data, Analytics, and AI team members foster a unified understanding of customers, actions, and outcomes by integrating analytics and insights seamlessly into CMG’s evolving digital, data, and automation platforms, creating scalable solutions and eliminating silos.

In Data, Analytics, and AI, you will work as a trusted, objective advisor and expert, recommending critical decisions and actions to be taken with credibility and a focus on driving measurable impact. You will be part of a thriving culture built on collaboration and innovation.

Job Summary

The Senior Data Product Manager is responsible for the development, execution, and optimization of data products within the organization. This role focuses on translating business needs into data product requirements, managing the product lifecycle, and ensuring alignment with broader organizational goals. By collaborating with cross‑functional teams, stakeholders, and business leaders, the Senior Data Product Manager drives data product enhancements and optimizes their impact across multiple use cases.

Key Responsibilities

• Partner with the Principal Data Product Owner to support the creation of data product roadmaps, including features, timelines, and dependencies.
• Develop and communicate a clear, value‑driven product vision that aligns with broader organizational objectives and business needs.
• Act as a point of contact for inquiries and demand requests related to our data products, effectively serving as the liaison between business stakeholders and the data product technology team.
• Monitor and measure the performance of the data product in collaboration with IT partners, identifying opportunities for optimization and ensuring it consistently delivers value to stakeholders.
• Collaborate with cross‑functional teams and technology partners to identify and acquire data sources that meet business product requirements and strategic objectives, ensuring alignment with broader data enablement strategies.
• Maintain and prioritize the data product backlog, ensuring it addresses the requirements of both single and multiple business use cases enabled by the product.
• Collaborate with cross‑functional teams to design, test, and release data product enhancements, ensuring data quality, usability, and seamless integration with business workflows.
• Guide the cross‑functional team in adhering to data mesh and data fabric principles, ensuring the product complies with established standards and supports scalability.
• Engage with business leaders and end‑users to advocate for the data product, fostering trust and collaboration.
• Facilitate the adoption and integration of tools and platforms that support data exploration, analytics, and visualization, in collaboration with technical counterparts.
• Establish and maintain a comprehensive data quality framework that aligns with Genentech’s strategic objectives and regulatory requirements.
• Collaborate with data governance teams to ensure proper stewardship and accountability for data assets.
• Define key data quality metrics and partner with relevant teams to ensure visibility into data integrity and performance.
• Identify, prioritize, and address data quality issues, driving root‑cause analysis and remediation efforts.
• Partner with data stewards, data engineers, and business units to understand data needs and challenges.
• Act as a liaison between technical and non‑technical teams to ensure alignment on data quality priorities.
• Foster a culture of data…