Senior Product Manager - Platform

Job Summary

At Arctic Wolf, you won t just watch the cybersecurity industry evolve - youll help lead the change. Our global Pack is made up of people who thrive on solving hard problems, moving fast, and building technology that protects organizations around the world. We re proud to be recognized by Forbes, CNBC, Fortune, CRN, Bartner Peer Insights and IDC MarketScape - but what matters most is the work behind it: delivering real outcomes for customers through award winning innovation like our Aurora Platform.

If you re looking for meaningful work, smart teammates and the chance to make a real impact in a high-growth company that s redefining security operations, Arctic Wolf is the right place for you!

Our mission is simple: End Cyber Risk. We re looking for a Product Manager-Platform to be part of making this/that happen.

Position Overview and Objective

This Technical Product Manager role drives requirements, execution, and delivery for reliable, scalable data lake infrastructure and intelligent AI/ML capabilities that power Arctic Wolf s security products and platform operations. The TPM translates complex business, security, and operational needs into clear product requirements and technical specifications-initially supporting 1-2 engineering squads (scaling to 2+) across sprint planning, backlog refinement, prioritization, and milestone tracking. The role collaborates closely with Data Engineers, Data Science Engineers, Platform Architects and cross-functional stakeholders to support use cases such as log availability, telemetry analysis, threat detection, and intelligent alerting while ensuring solutions adhere to robust data security, privacy, and governance standards. The role requires foundational technical fluency in distributed data systems and AI/ML lifecycles, Agile execution, and the ability to represent data platform capabilities authoritatively to integrating teams.

Responsibilities

• Works with senior PMs to understand how data platform infrastructure and AI-enabled capabilities align with the broader platform vision and roadmap.
• Gathers and authors requirements, PRDs, user stories, and acceptance criteria for data pipelines, query services, data lake access layers, and AI/ML model integration workflows. Defines project-level success criteria.
• Owns the product backlog and delivery execution for 1-2 engineering squads (growing to 2+), managing prioritization, dependencies, risk mitigation, and on-time release milestones.
• Collaborates with stakeholders (Platform Engineering, Data Science teams, Security Operations, and Architecture) to assess system capabilities, onstraints, and architectural trade-offs.
• Validates, where possible, that High-Level Designs (HLDs) cover all validated use cases, data processing workflows, and performance requirements for platform features.
• Presents direction, roadmaps, and delivery status for data platform and intelligent capabilities to development teams and consuming product stakeholders.
• Answers authoritatively questions from teams integrating against data platform APIs, pipelines, and data access layers: what is supported today, what needs to be specified, and what is out of scope.
• Identifies high-demand, heavy-lift platform requests, cost-optimization opportunities, and emerging AI use cases for consideration in future strategy and planning sessions.

Knowledge & Experience

• Knowledge of Product Management discipline, requirements definition, and Agile/Scrum ceremonies and practices.
• Educational background in Computer Science or Software Engineering, or 2-5 years of equivalent technical/product experience.
• Foundational understanding of distributed data systems, data pipelines, relational/non-relational data modeling, REST APIs, and cloud infrastructure components (e.g., AWS, GCP).
• Familiarity with AI/ML fundamentals and lifecycle concepts (e.g., training datasets, model evaluation metrics, inference pipelines, anomaly detection, or NLP).
• Understanding of data governance, security, privacy, and compliance standards.
• Begins to develop a system-level approach, understanding how data platform architecture, query performance, and indexing decisions ripple across consuming security products and operational costs.
• Exposure to security concepts such as logging pipelines, telemetry analysis, monitoring, or threat detection systems a plus.

Complexity and Problem Solving

• Collaborates with Engineering Managers, Tech Leads, Data Science Engineers, and internal stakeholders to maintain backlog priority, sizing, and acceptance criteria for data platform and AI enablement work.
• Communicates and escalates unexpected delays, cross-team blockers, data quality issues, or non-prioritized asks from integrating teams.
• Analyzes platform telemetry, query latency/throughput, data reliability metrics, and performance signals to determine the highest-value backlog items and platform optimizations.

Collaboration & Interaction

• Partners with Engineering to define technical acceptance criteria and ensure tickets/epics are properly documented before scheduling into sprints.
• Collaborates with consuming product teams and operational stakeholders to define success criteria and validate that data platform and AI features resolve the intended operational challenges.
• Engages Engineering to monitor telemetry related to platform usage, storage/compute efficiency, and AI effectiveness, reporting regularly on key operational metrics.
• Develops strong working relationships with Engineering and Data Science teams as a trusted technical subject-matter partner, and proactively collaborates with peer Product Managers as well as cross functional teams.

Why This Role

This is real technical ownership, not requirements administration. Youll partner directly with engineering on systems that the rest of the platform depends on, with genuine architectural input: reviewing HLDs, shaping tradeoffs, and building prototypes to test direction before engineering commits. Youll join a small team at the ground floor of one of the first governed AI-access layers in the security industry, with a growth path shaped by how fast this space matures, not a fixed org chart.

On-Camera Policy

To support a fair, transparent, and engaging interview experience, candidates interviewing remotely are expected to be on camera during all video interviews. Being on camera fosters authentic connection, improves communication, and allows for full engagement from both candidates and interviewers. We understand that technical, bandwidth, or location-related challenges may occasionally prevent video use. If this applies, candidates are required to notify us in advance so we can explore appropriate accommodations.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.