New opportunity

AI Technical Product Manager, AVP

State Street · Burlington, United States of America

About this role

The Observability AI Technical Product Manager, AVP, owns the observability intelligence roadmap for State Street’s Charles River Investment Management Solution. This hands-on product role uses Dynatrace Intelligence, including Davis AI and Grail, to improve platform availability, performance, cost efficiency and operational automation across an investment management platform.

Responsibilities

Own product vision, strategy, requirements, prioritization, delivery and value realization for AI-driven observability. Partner with SaaS, engineering, SRE, support and platform teams to develop capabilities and drive adoption across products and clients.
Turn operational problems into use cases for anomaly detection, causal and root cause analysis, event correlation, predictive alerting and capacity planning, business impact analysis, and automated remediation. Influence instrumentation, data quality and AI signal fidelity.
Configure and tune Davis AI, DQL queries, Grail data models and Gen3 dashboards. Validate AI outputs, reduce alert noise and improve problem-card precision using logs, metrics, traces, topology and business events.
Define KPIs for availability, MTTR, alert quality, cost and automation; support SLOs, error budgets and operational readiness. Work with SRE and operations teams to incorporate insights into runbooks and automation.
Communicate roadmaps and outcomes to internal stakeholders, strategic clients and executives; incorporate client feedback. Train teams and establish governance for AI models, alerting standards and observability data usage.

Required qualifications

A Bachelor of Science or Engineering degree, or equivalent experience, plus at least 8 years in platform engineering, observability, reliability or technical product management roles. At least 5 years of hands-on Dynatrace SaaS experience, including Davis AI, DQL, Grail and log analytics. Requires knowledge of AI/ML for AIOps, distributed systems, microservices, cloud-native architectures and SaaS platforms, and the ability to translate technical capabilities into business outcomes.

Preferred

Kubernetes, cloud platforms, large-scale SaaS, automation, DevOps, CI/CD and infrastructure as code experience; financial services or regulated-enterprise background; executive communication and stakeholder management.

The posted annual salary range for the primary location is $80,000–$140,000; a different work location may have a different range. Benefits include a 401(k) with company match, insurance, paid time off, an employee assistance program and eligibility for performance-based awards.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Technical product managementDynatrace SaaSDynatrace IntelligenceDavis AIGrailDQLGen3 dashboardsAIOpsObservabilityAnomaly detectionRoot cause analysisEvent correlationPredictive analyticsAutomated remediationLog analyticsDistributed systemsMicroservicesCloud-native architectureSite reliability engineeringSLOsKubernetesDevOpsCI/CDInfrastructure as CodeStakeholder managementExecutive communication

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Own the observability intelligence roadmap
The role owns vision, requirements, prioritization, delivery and value realization for Dynatrace Intelligence across the Charles River platform. Preparation focus: explain how to turn operational problems into a sequenced roadmap with measurable outcomes.
Make AI-driven signals actionable
The posting calls for hands-on tuning of Davis AI, DQL queries, Grail data models and Gen3 dashboards, using logs, metrics, traces, topology and business events to improve alert and problem-card precision. Preparation focus: be ready to explain how outputs are validated and noise is reduced.
Connect observability to reliability outcomes
The role defines KPIs for availability, MTTR, alert quality, cost and automation; supports SLOs and error budgets; and works with SRE and operations to incorporate insights into runbooks and automation. Preparation focus: distinguish telemetry improvements from operational results.
Balance client needs with platform scale
The product manager is the observability intelligence interface for internal stakeholders and strategic clients, incorporating feedback while maintaining platform standards and scalability. Preparation focus: show a prioritization approach that makes those trade-offs explicit.
Drive adoption and governance
The posting assigns enablement across product, support and engineering teams, plus governance for AI models, alerting standards and observability data usage. Preparation focus: explain how shared practices would make insights usable and consistent across teams.

Likely challenges to think through

Precision versus alert coverage
A likely challenge is reducing noisy alerts without overlooking consequential incidents; the posting specifically emphasizes signal fidelity and problem-card precision. Prepare a method for checking AI conclusions against multiple telemetry sources and assessing both false positives and missed signals.
Automation versus operational control
Turning causal insights into remediation could shorten MTTR but introduces the risk of acting on an incorrect diagnosis. Prepare to explain when an insight should inform a runbook versus trigger automation, and how its effect would be measured.
Client feedback versus reusable standards
Strategic clients may value different observability outcomes, while the posting calls for scalable platform standards. Prepare a prioritization framework that weighs client impact, reliability value and reuse without assuming every request becomes a custom capability.

Practice your answers

Technical practice

How would you prioritize an observability intelligence roadmap for the Charles River platform?

What to coverPreparation suggestion: Explain how you would turn operational and business problems into requirements, weigh reliability impact against delivery effort, and define measurable outcomes. Address how input from SaaS, engineering, SRE, support, and clients would inform prioritization.

How would you improve instrumentation and data quality for AI-driven observability in a distributed SaaS platform?

What to coverPreparation suggestion: Discuss the roles of logs, metrics, traces, topology, and business events in producing usable signals across microservices. Explain how you would identify missing context and assess its effect on AI signal fidelity.

How would you use DQL, Grail data models, and Gen3 dashboards to investigate a platform issue?

What to coverPreparation suggestion: Walk through how you would select and relate relevant observability data, then present findings that support an operational decision. Distinguish what the data establishes from what still needs validation.

How would you tune Davis AI to reduce alert noise without missing consequential anomalies?

What to coverPreparation suggestion: Describe how you would validate AI outputs against operational evidence and assess alert quality and problem-card precision. Discuss the trade-off between fewer false alarms and timely detection.

How would you test an AI-generated root cause analysis for a service disruption?

What to coverPreparation suggestion: Explain how event correlation, traces, logs, and topology could support or challenge a proposed causal explanation. Separate a correlated symptom from a plausible cause and describe what evidence would change your conclusion.

How would you evaluate predictive capacity alerts for a large-scale SaaS service?

What to coverPreparation suggestion: Discuss the signals and historical patterns you would examine, along with how you would check forecast usefulness against observed demand. Address the cost of premature capacity action versus the risk of acting too late.

When should an AI-driven insight trigger automated remediation rather than a runbook recommendation?

What to coverPreparation suggestion: Compare confidence in the diagnosis, potential impact, and reversibility of the action. Explain how SRE and operations teams could validate the result and keep automation aligned with operational readiness.

How would you demonstrate whether observability intelligence improves reliability and cost efficiency?

What to coverPreparation suggestion: Connect availability, MTTR, alert quality, cost, and automation KPIs to a baseline and a clear outcome measure. Explain how SLOs and error budgets would inform trade-offs rather than treating dashboard activity as value by itself.

How would you connect a technical anomaly to business impact for a strategic client?

What to coverPreparation suggestion: Describe how business events and service evidence could help establish which platform functions are affected and how confidently. Explain how you would communicate the impact without claiming more certainty than the data supports.

How would you govern AI models, alerting standards, and observability data usage across teams?

What to coverPreparation suggestion: Discuss standards for reviewing model outputs, maintaining actionable alerts, and using observability data consistently. Address the trade-off between common platform practices and teams’ differing operational needs.

Behavioural practice

Tell me about a time you aligned engineering, SRE, and support around a reliability priority.

What to coverPreparation suggestion: Use a real situation, your specific actions to reconcile competing views, and the result. Include how you established the priority and assessed whether it delivered operational value.

Describe a time client feedback conflicted with a scalable platform approach.

What to coverPreparation suggestion: Explain the situation, how you weighed the client’s need against broader product standards, and what you decided or influenced. Close with the result and what the feedback changed.

Tell me about a time you communicated a complex technical outcome to executives or clients.

What to coverPreparation suggestion: Set out the audience and decision at stake, the actions you took to make the evidence understandable, and the result. Distinguish measured outcomes from uncertainty or work still in progress.

Describe a time you helped teams adopt a new operational capability.

What to coverPreparation suggestion: Give a real example covering the initial adoption barrier, the training or enablement actions you took, and the result. Explain how you determined whether teams could use the capability effectively.

Tell me about a time you established an operational standard despite differing team preferences.

What to coverPreparation suggestion: Describe the situation, how you worked through objections, and your role in agreeing on a practical standard. State the result, including any trade-off you accepted.

Sharpen your knowledge

Topics to prepare

AIOps causality and signal quality
Revision exercise (suggested): For a hypothetical latency spike, explain how anomaly detection, event correlation and causal analysis differ, then identify evidence that would confirm or challenge an AI-generated root cause. Relevant because the posting requires AIOps knowledge and emphasizes explainable insights with less alert noise.
Distributed SaaS observability
Revision exercise (suggested): Sketch a microservices request path and map where logs, metrics, traces, topology and business events would help diagnose a failure. Relevant because the role requires distributed-systems and SaaS knowledge and uses these signals to improve AI output fidelity.
Reliability measures and trade-offs
Revision exercise (suggested): Define a hypothetical availability SLO and error budget, then explain how MTTR, alert quality, cost and automation measures would show whether an observability change helped. Relevant because the role owns these KPIs and supports operational readiness.
Observability product strategy
Revision exercise (suggested): Compare two hypothetical requests—better root cause analysis and predictive capacity—and explain how client impact, reliability value, scalability and delivery effort would affect roadmap priority. Relevant because the role owns strategy through value realization for the Charles River platform.
AI observability governance
Revision exercise (suggested): Outline review questions for AI output validation, alerting standards and permitted observability-data use, including who needs enablement. Relevant because the posting assigns governance and training across product, support and engineering teams.

Skills to revise

DQL and Grail analysis
Revision exercise (suggested): Using a hypothetical incident dataset, plan DQL queries and a Grail data model that connect log analytics with service and business-event context; state how you would check data quality. Relevant because hands-on DQL, Grail and log analytics are required.
Davis AI tuning
Revision exercise (suggested): Walk through a hypothetical set of duplicate or inaccurate problem cards, documenting what you would inspect, tune and compare against confirmed incidents. Relevant because the role configures Davis AI, validates its outputs and improves alert precision.
Gen3 dashboard design
Revision exercise (suggested): Sketch a Gen3 dashboard for an incident review showing availability, MTTR, alert quality and supporting telemetry, with a clear path from summary to evidence. Relevant because hands-on Gen3 dashboards and measurable reliability outcomes are stated responsibilities.
Roadmap and stakeholder communication
Revision exercise (suggested): Write a one-page hypothetical observability capability proposal with a problem statement, requirements, priority rationale, success measure and separate executive and engineering summaries. Relevant because the role owns product delivery and communicates value to technical, client and executive audiences.
Operationalizing insights
Revision exercise (suggested): Draft a hypothetical runbook decision path from a Davis AI finding to human verification, escalation and a bounded remediation action; specify how the outcome would be measured. Relevant because the posting calls for work with SRE and operations teams to turn insights into runbooks and automation.

Your preparation checklist

Map your evidence to the stated requirements
Prepare a one-page account of relevant roles, education or equivalent experience, and hands-on Dynatrace SaaS work. The posting specifies 8+ years in relevant roles and 5+ years with Dynatrace SaaS, including Davis AI, DQL, Grail and log analytics; document only experience you can substantiate.
Build a product ownership walkthrough
Select a real observability or reliability initiative you can discuss. Outline the operational problem, requirements, prioritization decisions, delivery partners and measured business outcome; distinguish your decisions from the team’s work.
Prepare a Dynatrace technical example
Bring a sanitized example, if available, showing how you configured or tuned Davis AI, used DQL and Grail, or built a Gen3 dashboard. Be ready to explain the telemetry used, how you checked AI conclusions, and what changed in alert noise or problem-card precision.
Sketch an AIOps design exercise
As interview practice, draft a hypothetical response to a service incident on a distributed investment-management platform. Show how logs, metrics, traces, topology and business events could support anomaly detection, causal analysis, client-impact assessment and a safe remediation decision; label assumptions.
Draft a measurable roadmap proposal
Prepare a short, illustrative roadmap that ranks observability use cases by reliability value, data readiness and delivery effort. Include proposed measures for availability, MTTR, alert quality, cost and automation, and explain how SLOs and error budgets would inform priorities.
Plan for adoption and governance discussions
Outline how you would work with SRE, support, engineering and clients to put insights into runbooks and gather feedback. Prepare talking points on team enablement, AI-model oversight, alerting standards and observability-data usage, all responsibilities named in the posting.
Write targeted questions for the employer
Ask which observability use cases are highest priority for Charles River, where instrumentation or data quality limits Davis AI today, how roadmap trade-offs are made across products and clients, and which reliability outcomes would define success for this role.

Availability last checked: Nov 4, 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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