Staff Product Manager, Agentic Data Platform

Horizon3.ai

. Define and execute the product vision and multi-quarter roadmap for NodeZero's data services and machine learning platform .

Posted 10/28/2026full-timeUnited StatesLead💰 $194,395 - $265,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in product management for data services and machine learning platforms, with a strong focus on scalable data architectures, governance, and financial performance. Proven ability to drive adoption and deliver customer value through strategic collaboration and data-driven decision-making.

Highest-signal resume keywords

Product Management ExperienceInfrastructure Cost ModelsData Governance RequirementsScalable Data PlatformsMachine Learning Infrastructure

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Relational DatabasesGraph DatabasesData PipelinesData ModelingAPIsDisaster RecoveryHigh AvailabilityMachine Learning InfrastructureData GovernanceFinancial Acumen

Soft Skills

Excellent CommunicationStakeholder ManagementCollaborationStrategic ThinkingData-Driven Decision-Making

Tools & Technologies

B2B SaaSAgile MethodologiesCloud InfrastructureMLOps TechnologiesFinOps Technologies

Industry Keywords

Data ServicesMachine Learning PlatformProduct VisionCustomer ValueOperational Resilience

Tech Stack

Tools & technologies

Cloud

About the role

Key responsibilities & impact

Define and execute the product vision and multi-quarter roadmap for NodeZero's data services and machine learning platform
Determine centralized capabilities, evaluate build-versus-buy decisions, and align platform investments with business priorities and customer outcomes
Lead managed relational, graph, and vector database services, including provisioning, scaling, migrations, upgrades, and lifecycle management
Own the unified asset and attack graph product strategy, including relationships, query performance, data freshness, consistency, and scalable access
Define scalable data models, query APIs, change-data-capture pipelines, event streams, schema governance, and platform abstractions
Own infrastructure for ML training pipelines, feature stores, experiment tracking, model registries, batch and real-time inference, deployment, monitoring, and evaluation
Define curated, versioned, and documented training datasets and drive data quality, lineage, reproducibility, labeling, ground-truth collection, and feature consistency
Drive high availability, scalability, disaster recovery, backup and restore, multi-region readiness, observability, SLOs, SLAs, and operational resilience
Define requirements for tenant isolation, multi-tenancy, access controls, encryption, retention, deletion, consent, anonymization, auditability, and model governance
Own infrastructure cost strategy, investment cases, TCO and ROI analysis, budgets, forecasts, cost visibility, optimization, and accountability
Own platform business performance, including ARR, retention, expansion, gross margin, pricing, packaging, monetization, and competitive positioning
Drive adoption across product and engineering teams, including migration from legacy solutions
Engage customers and stakeholders to understand requirements, improve activation and usage, and deliver measurable customer value
Collaborate with Engineering, ML Engineering, Design, Finance, Sales, Marketing, Customer Success, and executive leadership
Define and monitor KPIs such as adoption, availability, query performance, data freshness, time-to-production, reliability, cost per tenant, gross margin, retention, and revenue contribution
Lead regular business reviews and continuous improvement

Requirements

What you’ll need

8+ years of Product Management experience
4+ years building infrastructure, data, or machine learning platform products in B2B SaaS environments
Deep technical understanding of production databases, including relational and graph systems
Knowledge of replication, high availability, disaster recovery, indexing, and performance trade-offs
Experience with scalable data platforms, 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, and tenant isolation
Demonstrated financial acumen, including infrastructure cost models, total cost of ownership, ROI, investment business cases, and cloud infrastructure spending
Proven experience driving adoption of internal platforms and migrating users from existing solutions
Strong product judgment balancing technical constraints, architecture, reliability, customer needs, infrastructure costs, and platform investments
Ability to collaborate with senior engineering leaders, data scientists, and ML engineers
Strong business orientation connecting platform investments to customer value, efficiency, adoption, revenue growth, and financial performance
Excellent communication and stakeholder management skills
Ability to operate strategically and tactically across product vision, PRDs, technical requirements, architecture reviews, prioritization, and launch execution
Data-driven decision-making and experience defining operational and business metrics
Experience working in Agile environments and leading cross-functional initiatives
Preferred: database engineer, data engineer, ML engineer, SRE, or technical founder experience
Preferred: security data platform experience, including XDR, SIEM, exposure management, or offensive security products
Preferred: graph data modeling, entity resolution, vector databases, semantic search, labeling pipelines, ground-truth datasets, training data infrastructure, FinOps, MLOps technologies, and generative AI data requirements
A bachelor's degree is preferred, not required; an advanced degree is also preferred

Benefits

Comp & perks

Equity package in the form of stock options for all full-time roles
Health, vision, and dental insurance for employees and their families
Flexible vacation policy
Generous parental leave
Career development opportunities
Inclusive and collaborative work culture
Up to 25% travel opportunity/requirement