Principal Product Manager, Core Data Systems

Responsibilities

• Define the vision and roadmap for ATP’s core data systems products, aligned to business outcomes and platform scalability.
• Build and evolve data products such as:
  Robotics telemetry and event datasets (health, faults, tasks, utilization), Fleet and site performance datasets and metrics layers, Vision datasets (images/video) with metadata, labeling workflows, and training‑ready packaging, APIs and curated datasets that enable analytics and ML teams to move faster, Contextualization of data from the shopfloor and analytics.
• Partner with engineering, data science, and operations to define requirements for data quality, latency, reliability, and cost—especially for high‑volume image/video data.
• Drive standardization across systems and suppliers by defining schemas and data contracts that work across sites and robotics platforms.
• Establish a clear approach to data access and governance across OT and IT environments (security, privacy, retention, auditability).
• Measure adoption and impact—tracking product usage, data quality and availability, and operational outcomes enabled by your data products.
• Lead cross‑functional alignment and decision‑making across manufacturing/operations, engineering, cybersecurity, IT, and key partners—without needing formal people leadership.

• Bachelor’s degree in Engineering (Electrical, Mechanical OR Industrial preferred) OR a similar field (Physics, Computer Science, etc.)
• 10+ years of experience in product management and/or building data products at scale.
• Track record delivering data products or platform capabilities used by multiple teams (not one‑off dashboards).
• Strong understanding of data product fundamentals: event data, batch vs streaming, data modeling, metadata/lineage, and data quality/observability.
• Ability to translate complex, ambiguous problems into clear priorities, requirements, and measurable outcomes.
• Strong communication and stakeholder leadership skills, including comfort working with both executives and technical teams.
• Master’s degree or MBA (even better).
• Experience with robotics, automation, or industrial telemetry data (uptime, MTTR, utilization, task success).
• Experience building products for vision/ML data (image/video pipelines, labeling workflows, dataset quality and versioning).
• Experience bridging OT and IT environments, including edge constraints and secure data movement.
• Familiarity with governance and security patterns (RBAC/ABAC, encryption, retention, audit controls) in enterprise settings.
• Experience enabling ML teams through well‑designed data interfaces, training data practices, or MLOps‑adjacent workflows.