Director, Product Management, Data Compute, Google Cloud Security

Director, Product Management, Data Compute, Google Cloud Security corporate_fare Google place Sunnyvale, CA, USA ; New York, NY, USA ; +2 more ; +1 more bar_chart Director+ info_outline

X Note:

By applying to this position you will have an opportunity to your preferred working location from the following: Sunnyvale, CA, USA; New York, NY, USA; Reston, VA, USA .

Minimum qualifications:

Bachelor's degree in Computer Science, a related technical field, or equivalent practical experience. 15 years of experience in product management, including 5 years of experience leading data or cloud infrastructure products. Experience building and scaling distributed systems, data lakes, or compute platforms. Experience managing teams of product managers in an enterprise environment.

Preferred qualifications:

Master's degree, MBA, or PhD in a technical field. Experience managing industry-standard schemas (e.g, OCSF) and telemetry frameworks. Experience in SaaS monetization strategies, particularly usage-based or token-based billing models. Expertise in the cybersecurity landscape, specifically in SIEM, XDR, or Security Analytics. Expertise in graph database technologies and their application in security entity modeling. Excellent communication skills with the ability to influence cross-functional stakeholders across a matrixed organization. About the job The Director of Product Management for Data and Compute Platforms joins Google Cloud Security, reporting to the VP of Product Management. This organization, known as GUSTO (Google Unified SecOps and Threat Operations), leads the creation of a unified, AI-powered security platform. The team directs the strategy and development for our security portfolio, including Chronicle, Mandiant, and native cloud services. Our mission is to transform extensive security expertise and threat intelligence into products that empower customers against evolving cyber threats. We are investing in AI to integrate it into our core products and attract talent. You will be instrumental in shaping the future of AI within Google's security ecosystem. Google Cloud Security protects global information. As Director of Product Management for the Data/Compute Platform, you will lead the foundational engine powering security operations, analytics, and threat intelligence. You will build a unified data fabric enabling ingestion, storage, and processing across multi-cloud environments. In this high-impact role, you will balance adoption of standards like OCSF with OpenTelemetry capabilities. You will also lead the strategic transition from legacy consumption to value-driven, token-based pricing, ensuring scalability for our global customers. Google aims to build products that organize the world's information and make it universally accessible to our users. As a Product Manager at Google, you could be working on new technologies, platforms, consumer facing products, and/or enterprise systems. The end goal will be to match you with the team that best aligns with your interests, experience, and where you will have the most impact. The US base salary range for this full-time position is $282,000-$392,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can more about the specific salary range for your preferred location during the hiring process. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about . Responsibilities Lead product outlook for a comprehensive, AI-first evaluation platform that establishes industry gold standard for measuring accuracy, safety, and reasoning capabilities of autonomous security agents. Define metrics, benchmarks, and progression criteria required to transition AI agents from human-in-the-loop (HITL) recommendations to fully autonomous, closed-loop execution in enterprise environments. Design highly scalable, automated testing pipelines that subject detection and response models to adversarial simulations, automated red-teaming, and historical incident replay to ensure resilience before and after production deployment. Develop the underlying logic and guardrails that calculate confidence scores for agentic decisions, dictating when an AI can safely act on its own versus when it escalates to a human analyst to mitigate risk.