Staff Product Manager, Agentic Data Platform

Horizon3•San Francisco, CA

3d•$194,395 - $265,000•Remote

About The Position

Horizon3 is seeking a Staff Product Manager to lead the strategy, development, and evolution of NodeZero’s Agentic Data Platform, including Database-as-a-Service (DBaaS), machine learning infrastructure, and data services that support new cybersecurity capabilities. The role turns insights generated by autonomous penetration tests into a scalable, reliable, and intelligent platform for product teams, machine learning engineers, and agentic systems. The product manager owns the vision and roadmap for core data services, including managed relational, graph, and vector databases; the unified asset and attack graph; data APIs; and infrastructure for traditional machine learning development and deployment. This includes training-ready datasets, feature stores, model training pipelines, deployment, monitoring, and governance. The role also owns the platform’s financial and operational strategy, partnering with Engineering, Finance, and executive leadership to evaluate investments, manage platform economics, and prioritize capabilities. This senior Product role requires technical depth in data infrastructure, database architecture, machine learning platforms, and cloud services, along with product judgment and commercial awareness. The role operates with high autonomy, influences executive direction, and defines success measures for platform adoption, performance, reliability, cost efficiency, and business growth. The company is fully remote; the position is located in New York or San Francisco and may require up to 25% travel.

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, and ability to discuss replication, high availability, disaster recovery, indexing, and performance trade-offs with engineers.
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 infrastructure cost modeling, total cost of ownership and ROI evaluation, investment business cases, and partnering with Finance on cloud infrastructure spending.
Proven experience driving adoption of internal platforms, establishing shared services, enabling engineering teams, and migrating users from existing solutions.
Strong product judgment balancing technical constraints, architecture, reliability, customer needs, infrastructure costs, and long-term platform investments.
Ability to collaborate credibly with senior engineering leaders, data scientists, and ML engineers to define requirements, evaluate trade-offs, and guide complex technical initiatives.
Strong business orientation and experience connecting platform investments to customer value, operational efficiency, adoption, revenue growth, and financial performance.
Excellent communication and stakeholder management skills, including influencing executives, presenting investment recommendations, and translating technical concepts into business outcomes.
Comfort operating at 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, including 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 Haves

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.

Responsibilities

Define and execute the product vision and multi-quarter roadmap for NodeZero’s data services and machine learning platform; evaluate centralized capabilities and build-versus-buy decisions, aligning investments with business priorities, customer outcomes, and product differentiation.
Lead the evolution of managed relational, graph, and vector data services, including self-service provisioning, scaling, migrations, upgrades, and lifecycle management.
Own product strategy for NodeZero’s unified asset and attack graph, including data relationships, multi-hop query performance, freshness, consistency, and scalable access to security intelligence.
Partner with Engineering on data models, query APIs, change-data-capture pipelines, event streams, schema governance, and platform abstractions.
Own infrastructure strategy for 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; drive data quality, lineage, reproducibility, labeling pipelines, ground-truth collection, and consistent offline and online features.
Drive platform reliability and operational excellence, including high availability, scalability, disaster recovery, backup and restore, multi-region readiness, operational resilience, SLOs, data quality SLAs, observability, and performance metrics.
Define requirements for tenant isolation, hierarchical multi-tenancy, access controls, encryption, retention, deletion, and sensitive security artifact handling; support customer consent, anonymization, auditability, and model governance.
Own infrastructure cost requirements and business strategy; develop investment cases, evaluate total cost of ownership and ROI, partner with Finance on budgets and forecasts, and drive cost visibility and optimization.
Own platform business performance, including contribution to ARR, retention, expansion, and gross margin; identify pricing, packaging, monetization, and customer value opportunities; assess markets, competition, and investment priorities.
Drive adoption of shared data and ML capabilities across internal product and engineering teams, including migrations from legacy solutions; engage customers and stakeholders to understand requirements and improve activation and usage.
Collaborate with Engineering, ML Engineering, Design, Finance, Sales, Marketing, Customer Success, and executive leadership to translate strategy into requirements, architecture decisions, launch plans, and measurable outcomes.
Define and monitor KPIs such as adoption, data availability, query performance, freshness, model time-to-production, reliability, cost per tenant, gross margin, retention, and revenue contribution; lead business reviews and guide prioritization with operational, financial, and customer insights.
Operate at strategic and tactical levels, from platform vision and business strategy to PRDs, technical requirements, architecture reviews, prioritization, and launch execution.