Global Product Owner, Observability

BMO•Toronto, ON

1d•CA$121,600 - CA$211,800•Hybrid

About The Position

The Global Product Owner, Observability is accountable for defining, owning, and executing the enterprise observability strategy for the bank. This role owns the enterprise-wide observability roadmap, ensuring consistent, scalable, and cost-effective observability capabilities are delivered across all technology domains. Operating with enterprise influence, this role provides strategic direction and dotted-line leadership to three horizontal capability teams—Enterprise Monitoring Tools, Application Performance Monitoring (APM), and Site Reliability Engineering (SRE)—ensuring their priorities, investments, and execution are aligned to the enterprise roadmap. The role regularly engages with senior and executive leadership, translating complex technical concepts into clear business outcomes, progress updates, and value realization.

Requirements

Senior-level experience owning enterprise-scale technology platforms or products.
Deep hands-on experience with Dynatrace, including APM, infrastructure monitoring, and advanced observability use cases.
Strong experience with cloud-native observability tooling in Azure and AWS environments.
Proven track record delivering and governing multi-year roadmaps across large, federated organizations.
Strong executive-level communication skills, with experience presenting to senior leadership forums.
Demonstrated success in platform rationalization, vendor management, and cost optimization.

Nice To Haves

Experience in financial services or other highly regulated enterprise environments.
Strong exposure to SRE operating models, reliability engineering, and automation at scale.
Background working within large, matrixed, global technology organizations.

Responsibilities

Define, maintain, and evolve the bank-wide observability vision, strategy, and multi-year roadmap spanning infrastructure, platforms, applications, and digital services.
Establish enterprise observability standards, patterns, and guardrails to ensure consistency, scalability, and reliability across all technology domains.
Ensure observability capabilities extend to AI-enabled systems, providing visibility into model behavior, performance, and operational health as part of the broader observability strategy.
Align the observability roadmap with cloud strategy, reliability objectives, security requirements, and business priorities.
Provide dotted-line accountability and strategic direction for Enterprise Monitoring Tools, APM, and SRE teams.
Ensure team roadmaps, backlogs, and delivery plans align with the enterprise observability strategy, including coverage for cloud-native and AI-enabled workloads.
Drive horizontal alignment across engineering, operations, architecture, data, and platform teams in a federated environment.
Define enterprise-level KPIs, success metrics, and maturity measures for observability, reliability, and operational resilience.
Ensure observability metrics include appropriate coverage for AI-enabled services, such as model stability, drift indicators, and operational risk signals where applicable.
Monitor and measure progress against the roadmap, including adoption, benefits realization, risk reduction, and cost efficiency.
Prepare and present clear, outcome-focused updates to executive-level audiences, translating technical signals into business and risk-focused insights.
Optimize observability platform efficiency, performance, and cost across multiple vendors.
Lead simplification and standardization efforts to reduce tooling sprawl and operational complexity.
Ensure observability platforms and practices are easily scalable up and down to support changing business demand and consumption patterns.
Advance self-healing and auto-remediation capabilities using actionable telemetry, automation, and policy-driven responses across platforms and services.
Partner with SRE and platform teams to reduce manual operational effort and improve mean time to detect and remediate incidents.
Leverage observability signals—including those from AI-enabled systems—to support intelligent automation and controlled remediation.
Drive the long-term progression toward NoOps-aligned operating models, where observability, automation, and governance enable safe, scalable, and repeatable operations.
Ensure observability capabilities support governance, security, and risk management, including traceability, logging, and auditability.
Establish observability requirements for AI-enabled systems to support explainability, accountability, and model risk management.
Partner with security, risk, data, and architecture teams to ensure compliance with enterprise security, privacy, and regulatory standards.