Senior Product Manager, Data Infrastructure and Analytics

As a Senior Product Manager, Data Infrastructure and Analytics at CoreWeave, you'll lead the development of our Enterprise Data Platform, focusing on architecting a scalable data lake and overseeing data ingestion, transformation, and governance. This role requires collaboration across engineering, analytics, and business teams, ensuring the seamless movement of data and facilitating key business insights.

Candidates for the Senior Product Manager role at CoreWeave are expected to have 8+ years of experience in data product management or data architecture, strong expertise with data lakes, ETL/ELT pipelines, and familiarity with modern data stack technologies like Snowflake and Databricks. Deep knowledge of data modeling and governance frameworks is crucial, along with proven collaboration skills across engineering and analytics domains.

At CoreWeave, we prioritize investing in our people. As a Senior Product Manager, you will have access to ongoing training, mentorship, and opportunities to work on innovative projects that challenge your skills. We encourage a culture of continuous learning and provide resources to help you grow professionally while making a substantial impact in the field of data management.

CoreWeave embraces a hybrid workplace model, allowing Senior Product Managers to enjoy flexibility between in-office and remote work. This approach fosters collaboration and creativity while also respecting individual work-life balance preferences. For those located outside of our office areas, we may consider remote arrangements if your experience aligns strongly with the role.

For the Senior Product Manager, Data Infrastructure and Analytics position at CoreWeave, the base pay typically ranges from $160,000 to $190,000, depending on various factors such as location, experience, and skills. We also offer a comprehensive benefits package to support your overall well-being and professional growth.

When answering this question, focus on specific projects where you designed or managed data lakes. Highlight your understanding of architecture decisions, data ingestion, and governance protocols. Be sure to articulate the challenges you faced and how you optimized performance or data accessibility.

Explain your approach to data governance, including how you establish best practices for data quality, lineage, and security. Discuss tools you've implemented and any successful frameworks you developed to maintain high standards across data products, emphasizing collaboration with cross-functional teams.

In response to this question, outline your method for gathering requirements and understanding the priorities of various stakeholders. Discuss how you balance these demands by aligning data initiatives with broader business goals, highlighting any frameworks you use to manage and prioritize tasks effectively.

Share a specific example of a data product you took from concept to completion. Detail the challenges you faced, your approach to problem-solving, and how you engaged with various teams. Emphasize any metrics or success stories that illustrate the product's impact post-launch.

Articulate your strategy for continuous learning about new technologies, whether it's through industry publications, attending conferences, or engaging with professional communities. Mention specific instances where you've adopted new tools or methodologies that influenced your work positively.

Outline the critical components of a data ingestion pipeline, such as scalability, reliability, and real-time processing. Discuss best practices for handling structured and unstructured data, emphasizing your experience with various ETL/ELT tools and frameworks to optimize performance.

Discuss your techniques for fostering open communication and collaboration, such as regular cross-functional meetings and collaborative tools. Highlight specific examples where you successfully brought together diverse teams to achieve project objectives, navigating differing perspectives effectively.

Express your understanding of domain-driven design principles and how you apply them in structuring data products. Provide examples of how you've utilized this approach to create modular, scalable products that align with business objectives and improve collaboration across teams.

Identify key performance indicators (KPIs) that relate to data quality, user satisfaction, and operational efficiency. Discuss how you've used data analytics to measure and report on these metrics in previous roles, adjusting strategies as needed to enhance overall product success.

Discuss your philosophy around onboarding, emphasizing the importance of acclimating new hires to the company culture, processes, and tools. Provide a structured overview of how you would facilitate their integration, including training programs and mentorship opportunities.