Staff Product Manager, Agentic Data Platform

Horizon3 is a fast-growing, remote cybersecurity company. The Staff Product Manager will lead the strategy, development, and evolution of NodeZero's Agentic Data Platform, including Database-as-a-Service (DBaaS), machine learning infrastructure, and the data services that power next-generation cybersecurity capabilities. The role owns the vision and roadmap for NodeZero's core data services, the unified asset and attack graph, data APIs, and the infrastructure supporting traditional machine learning development and deployment, as well as the financial and operational strategy behind the platform.

Responsibilities

Define and execute the product vision and multi-quarter roadmap for NodeZero's data services and machine learning platform; Lead the evolution of managed relational, graph, and vector data services, including self-service provisioning, scaling, migrations, upgrades, and lifecycle management; Own the product strategy for NodeZero's unified asset and attack graph, including complex data relationships, multi-hop query performance, data freshness, consistency, and scalable access to offensive security intelligence; Partner with Engineering to define scalable data models, query APIs, change-data-capture pipelines, event streams, schema governance, and platform abstractions; Own the infrastructure supporting traditional machine learning workflows, including training pipelines, feature stores, experiment tracking, model registries, batch and real-time inference, deployment, monitoring, and evaluation standards; Define capabilities for curated, versioned, and documented training datasets derived from pentest and platform data; Drive the capabilities required for high availability, scalability, disaster recovery, backup and restore, multi-region readiness, and operational resilience; Define product requirements for tenant isolation, hierarchical multi-tenancy, data access controls, encryption, retention, deletion, and sensitive security artifact handling; Own the product requirements and business strategy for managing data and ML infrastructure costs; Own the platform's business performance, including its contribution to ARR, retention, expansion, and gross margin; Drive adoption of shared data and ML capabilities across internal product and engineering teams, including migration from legacy solutions; Collaborate with Engineering, ML Engineering, Design, Finance, Sales, Marketing, Customer Success, and executive leadership to translate product strategy into clear requirements and measurable outcomes; Define and monitor KPIs including platform adoption, data availability, query performance, data freshness, model time-to-production, infrastructure reliability, cost per tenant, gross margin, retention, and revenue contribution

Requirements

8+ years of Product Management experience, including 4+ years building infrastructure, data, or machine learning platform products in B2B SaaS environments; Deep technical understanding of production databases, including relational, graph, or similar systems, with the ability to engage engineers on replication, high availability, disaster recovery, indexing, and performance trade-offs; Experience building or managing scalable data platforms, including data pipelines, data modeling, schema evolution, data contracts, APIs, and distributed data architectures; Strong understanding of traditional machine learning infrastructure, including training data pipelines, feature stores, experiment tracking, model registries, batch and real-time inference, and production model monitoring; Experience defining data governance and security requirements, including lineage, anonymization, retention, deletion, tenant isolation, and responsible handling of sensitive data in multi-tenant SaaS environments; Demonstrated financial acumen, including developing infrastructure cost models, evaluating total cost of ownership and ROI, building investment business cases, and partnering with Finance on cloud infrastructure spending; Proven experience driving adoption of internal platforms, including establishing shared services, enabling engineering teams, and successfully migrating users from existing solutions; Strong product judgment in balancing technical constraints, architectural decisions, reliability, customer needs, infrastructure costs, and long-term platform investments; Ability to work credibly and collaboratively with senior engineering leaders, data scientists, and ML engineers to define requirements, evaluate trade-offs, and guide complex technical initiatives; Strong business orientation with experience connecting platform investments to customer value, operational efficiency, adoption, revenue growth, and financial performance; Excellent communication and stakeholder management skills, including the ability to influence executives, present investment recommendations, and translate complex technical concepts into clear business outcomes; Comfort operating at both strategic and tactical levels, from platform vision and business strategy to PRDs, technical requirements, architecture reviews, prioritization, and launch execution; Data-driven decision-making skills with experience defining operational and business metrics and using insights to drive product improvements; Experience working in Agile environments and leading cross-functional initiatives in fast-paced, evolving organizations

Nice to have

Previous experience as a database engineer, data engineer, ML engineer, Site Reliability Engineer (SRE), or technical founder; Experience with security data platforms, including XDR, SIEM, exposure management, or offensive security products; Hands-on familiarity with graph data modeling, entity resolution, vector databases, and semantic search; Experience developing labeling pipelines, ground-truth datasets, or training data infrastructure; Experience with FinOps practices, cloud infrastructure optimization, or FinOps certification; Familiarity with MLOps technologies such as MLflow, Kubeflow, SageMaker, Vertex AI, Databricks, or Feast; Understanding of generative AI data requirements, including embeddings, vector search, and fine-tuning datasets; Bachelor's degree in Computer Science, Engineering, Data Science, or a related technical field; An advanced degree in engineering, computer science, machine learning, or business is preferred

Benefits

Competitive compensation; Equity package in the form of stock options; Health, vision & dental insurance for you and your family; Flexible vacation policy; Generous parental leave; Growth opportunities; Inclusive team; Innovative culture