The Observability Platform team is seeking a Senior Manager of Engineering to build and lead a centralized platform capability that gives Smartsheet full-stack visibility into our most complex and consequential systems. This role owns the engineering strategy and execution for a dedicated platform consolidating multiple platforms, serving engineering teams across the company, including the Data & AI Platform, Commerce, and Infrastructure pillars.

You will lead a team based with strategic ownership spanning metrics, distributed tracing, alerting, log analytics, SLO/SLA management, and AI/ML observability integrations tied to SmartAssist and our agentic AI workstreams on Amazon Bedrock and MLflow. This is a high-leverage, high-visibility role at the intersection of platform reliability and AI-native engineering.

You Will:

Team & Platform Leadership

• Lead a team of engineers focused on observability platform engineering, driving build-out of a unified observability stack used by all engineering teams at Smartsheet.
• Own and evolve the platform's technical roadmap, consolidating multiple tooling platforms, and AI observability tooling into a coherent, scalable capability.
• Define platform standards, contribute to architectural direction, and ensure the team operates with engineering rigor and strong operational habits.
• Build and scale the team, hiring senior engineers and establishing effective global practices across distributed stakeholders.

Observability Engineering

• Lead design and delivery of centralized observability infrastructure covering metrics pipelines, distributed tracing, alerting frameworks, and log analytics across Smartsheet services.
• Drive SLO/SLA definition and tooling for platform-wide reliability visibility, partnering closely with infrastructure, platform engineering, and on-call teams.
• Own governance including instrumentation standards, cost optimization, and rollout of advanced capabilities such as APM, RUM, and custom dashboards.
• Lead architecture, scaling, and operational practices for log analytics across high-throughput production workloads.
• Establish shared observability libraries, agents, and SDKs that reduce instrumentation burden for application engineering teams.

AI Observability

• Build and maintain AI/ML observability integrations in partnership with the AI Platform team.
• Partner with the Data & AI Platform team to integrate MLflow tracing, Inference Tables, and LLM-as-judge evaluation pipelines into the observability stack.
• Develop dashboards and alerting for agentic AI workloads, including latency, token consumption, error rates, and evaluation metric drift.
• Contribute to the AI governance and cost observability program, providing telemetry for model usage, cost attribution, and compliance reporting.

Cross-Functional Partnership & Execution

• Serve as the primary engineering partner for platform consumers across Data & AI, Commerce, Infrastructure, and Security teams, ensuring observability needs are met across workstreams.
• Lead complex, cross-functional observability projects with high ambiguity, managing delivery risk, communicating clearly to senior stakeholders, and building alignment across teams.
• Partner with delivery partners to coordinate instrumentation across platform modernization and migration workstreams
• Contribute to quarterly and annual platform goals, reporting on key reliability and observability metrics to engineering leadership.
• Communicate platform status, risks, and roadmap progress to Engineering leadership and above audiences in a clear, executive-ready format.

Operational Excellence

• Embed on-call culture and incident management discipline into the team, ensuring clear runbooks, fast MTTR, and post-incident learning loops.
• Drive cost governance for observability tooling, including spend optimization and efficient resource management.
• Champion AI-assisted engineering practices within the team, applying tooling and automation to reduce toil and accelerate delivery.

You Have:

Required

• 10+ years of software or platform engineering experience, with strong fundamentals in distributed systems, infrastructure, and backend services.
• 3 years of engineering management experience, including direct team building, performance management, and cross-functional delivery ownership.
• Deep hands-on expertise with observability tooling: Datadog (APM, metrics, logs, alerting), OpenSearch or Elasticsearch, distributed tracing (OpenTelemetry or equivalent), and SLO/SLA management at scale.
• Proven experience operating observability platforms for high-availability, high-throughput production environments.
• Experience building and scaling engineering teams in distributed or international focus
• Strong execution track record on complex, cross-functional infrastructure programs with high ambiguity.
• Clear, direct communication (written and verbal) with both technical and non-technical audiences, including leadership and executive stakeholders.
• Proactive risk identification and status communication without prompting.
• Experience managing vendors, external delivery partners, and third-party integrations in a platform context.

Preferred

• Hands-on experience with AI/ML observability: MLflow tracing, LLM evaluation pipelines, or observability for agentic AI systems.
• Familiarity with Amazon Bedrock, ECS Fargate, or LangGraph-based multi-agent architectures.
• Experience with cloud cost governance and FinOps practices for observability tooling 
• Exposure to data platform observability and data quality monitoring in a lakehouse context 
• Experience establishing internal developer platforms, shared libraries, or platform-as-a-service offerings for application teams.
• Prior work in SaaS environments with enterprise compliance requirements (SOC 2, FedRAMP, HIPAA).

Education & Eligibility

• CS, Engineering, or equivalent degree, or commensurate practical experience.
• Legally eligible to work in the U.S. on an ongoing basis