Senior Observability Platform Automation & AI Operations Consultant

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Role: Senior Observability Platform Automation & AI Operations Consultant
Location: Dallas, TX (Hybrid/Remote – Dallas Preferred)
Duration: 12+ Months (Extension Possible)
Designation: Principal Architect
Experience: 15+ Years
Role Summary
We are seeking a senior consultant to build and scale the enterprise observability platform by establishing reusable observability foundations, telemetry standards, monitoring-as-code frameworks, automation capabilities, and AIOps-driven operational intelligence. The ideal candidate will have deep expertise in Dynatrace, Davis AI (AIOps), Observability Architecture, OpenTelemetry, and telemetry pipeline design.
Key Responsibilities
Standardize reusable onboarding patterns, templates, and monitoring-as-code assets.
Define telemetry standards, OpenTelemetry patterns, and observability health models.
Build scalable observability frameworks and reusable monitoring patterns.
Streamline integrations across Dynatrace, PagerDuty, ServiceNow, Azure, and CI/CD platforms.
Enable AIOps capabilities, automation, event correlation, and operational intelligence.
Collaborate with application, platform, DevOps, and infrastructure teams to drive observability adoption and platform outcomes.
Develop automation and API-driven integrations to improve consistency and reduce manual effort.

Required Skills
Must Have:
Strong hands-on experience with Dynatrace Administration, Automation, and API Integrations
Experience with Dynatrace Davis AI (AIOps), event correlation, root cause analysis, and intelligent alerting
Strong Observability & Monitoring Architecture experience
Expertise in OpenTelemetry, telemetry pipelines, distributed tracing, metrics, and logging standards
Experience building reusable observability patterns and monitoring frameworks
Hands-on experience with scripting, automation, Infrastructure-as-Code, and platform integrations
Experience with ServiceNow, PagerDuty, Azure, and CI/CD integrations
Exposure to ETL, Reporting Platforms, Data Pipelines, or AI/LLM Observability
Expected Outcomes
Standardize automation, telemetry, and integration standards across the observability platform.
Deliver reusable observability assets and monitoring frameworks.
Reduce operational overhead through automation and AIOps enablement.
Improve platform consistency, monitoring effectiveness, production readiness, and scalability.