Group Product Manager, Managed Service for Apache Spark

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
  
• 10 years of experience in product management or a related technical role.
  
• 5 years of experience taking technical products from conception to launch (e.g., ideation to execution, end-to-end, 0 to 1, etc.).
• 4 years of experience developing or launching products or technologies within one or more of the following: Apache Spark, Lakehouse, Hadoop, and other OSS analytical engines.

Preferred qualifications:

• Master's degree in a technology or business related field.
  
• 7 years of experience working cross-functionally with engineering, UX/UI, sales finance, and other stakeholders.
  
• 5 years of experience in software development or engineering.
  
• Strong foundational knowledge of data architectures supporting generative AI and autonomous systems, including RAG, and parallelized preprocessing for LLM training.
• Strong developer-centric focus to identify pain points and build intuitive AI experiences, with demonstrated ability to deliver compelling technical presentations to executive leadership and external customers.

About the job

At Google, we put our users first. The world is always changing, so we need Product Managers who are continuously adapting and excited to work on products that affect millions of people every day.

In this role, you will work cross-functionally to guide products from conception to launch by connecting the technical and business worlds. You can break down complex problems into steps that drive product development.

One of the many reasons Google consistently brings innovative, world-changing products to market is because of the collaborative work we do in Product Management. Our team works closely with creative engineers, designers, marketers, etc. to help design and develop technologies that improve access to the world's information. We're responsible for guiding products throughout the execution cycle, focusing specifically on analyzing, positioning, packaging, promoting, and tailoring our solutions to our users.

Google’s Agentic Data Cloud unifies customer data, AI models, and operational databases into a single System of Action. At the core of this offering is the Managed Service for Apache Spark, a premier compute engine enabling data engineering, streaming, and distributed machine learning over massive enterprise datasets. As enterprise AI shifts toward autonomous systems, Apache Spark is emerging as the foundational backbone for a wide range of high-impact Agentic AI workloads—including multimodal data prep, agent trajectory log processing for continuous learning, and low-latency context refresh for live agents.

Our team sits at the exhilarating intersection where massive-scale enterprise data and Agentic AI come together. Operating like a fast-moving startup inside Google Cloud, we are driving an accelerated wave of product innovations backed by planetary-scale infrastructure.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

Responsibilities

• Analyze next-generation shifts in enterprise AI to set and execute the multi-year roadmap for Managed Spark evolving the engine to power high-impact Agentic AI workloads.
• Advocate and represent the customer and define the end-to-end commercial strategy including pricing models, packaging, and unit economics to drive market share and customer return on investment (ROI).
• Collaborate with product and engineering leaders across Google Cloud Platform to deliver unified, interoperable capabilities that advance broader organizational goals and enterprise customer needs.
• Operate as a direct leader who dives into details, conducts deep quantitative analysis, and crafts foundational collateral.
• Advocate for a culture of experimentation and high-velocity execution within the product development lifecycle.