We are seeking a highly experienced Observability AI Technical Product Manager with deep hands-on expertise in Dynatrace Intelligence, including Davis AI, Grail, and advanced AIOps capabilities. This role blends technical product management with strong domain knowledge in observability, AI-driven operations, and platform reliability. The individual will define, own, and execute the observability intelligence roadmap, ensuring measurable outcomes across availability, performance, cost efficiency, and operational automation. This is a hands-on, technically grounded product role embedded within the fintech platform organization.
What you will be doing Partner with SaaS, engineering, SRE, technical support, and platform teams to define and deliver observability intelligence capabilities powered by Dynatrace Davis AI. Own the end-to-end product lifecycle including strategy, roadmap, requirements, prioritization, and value realization. Translate operational and business problems into AI-driven observability use cases such as anomaly detection, causal analysis, predictive alerting, automated remediation, and business impact analysis. Drive adoption of Dynatrace Intelligence across products and clients, ensuring observability data is actionable, explainable, and aligned with platform reliability objectives.
The Observability AI Technical Product Manager plays a critical role in advancing platform resilience and operational excellence by embedding AI-driven intelligence into how the platform is monitored, supported, and evolved. This role ensures observability is a strategic capability that improves client experience and accelerates decision-making.
- Key Responsibilities:Dynatrace Intelligence & AI Product Ownership
- Define and own the product vision and roadmap for Dynatrace Intelligence, including Davis AI, Grail, and AI-powered analytics. Drive use cases for anomaly detection, root cause analysis, event correlation, predictive capacity, and auto-remediation. Partner with engineering teams to influence instrumentation, data quality, and AI signal fidelity.
- AIOps & Hands-On Technical Leadership
- Hands-on experience configuring and tuning Davis AI, DQL queries, Grail data models, and Gen3 dashboards. Validate AI outputs, reduce noise, and improve precision of alerts and problem cards. Leverage logs, metrics, traces, topology, and business events to create causal, explainable insights.
- Platform & Reliability Outcomes
- Define KPIs tied to availability, MTTR reduction, alert quality, cost optimization, and operational automation. Ensure observability intelligence supports SLOs, error budgets, and operational readiness. Partner with SRE and operations teams to operationalize AI-driven insights into runbooks and automation.
- Stakeholder & Client Engagement
- Act as the primary product interface for observability intelligence with internal stakeholders and strategic clients. Communicate roadmap, value, and outcomes to executive, technical, and client audiences. Incorporate client feedback into prioritization while balancing platform standards and scalability.
- Training, Enablement & Governance
- Drive enablement across product, support, and engineering teams on AI-driven observability concepts and tooling. Establish governance for AI models, alerting standards, and observability data usage. Contribute to long-term observability and AIOps strategy aligned with platform modernization.
Required Qualifications: Bachelor of Science or Engineering (or equivalent experience) with 8+ years of experience in platform engineering, observability, reliability, or technical product management roles. 5+ years of hands-on experience with Dynatrace SaaS, including Davis AI, DQL, Grail, and log analytics. Strong understanding of AI/ML concepts as applied to AIOps, including anomaly detection, causality, signal-to-noise optimization, and predictive analytics. Deep knowledge of distributed systems, microservices, cloud-native architectures, and SaaS platforms. Proven ability to translate technical capabilities into product outcomes and business value.
Preferred Skills: Experience with Kubernetes, cloud platforms, and large-scale SaaS environments. Familiarity with automation, DevOps, CI/CD, and IaC practices. Background in financial services or regulated enterprise environments. Strong executive communication and stakeholder management skills.
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