Datadog Administration and Operations (Servicenow)

HP Inc. — Bengaluru, Karnātaka, India

Datadog Administration and Operations (Servicenow) Description - We’re seeking a Datadog administration and operations expert who will be responsible for managing our observability platform to ensure comprehensive monitoring, alerting, and performance analytics across infrastructure and applications. This role is critical for maintaining system reliability, improving incident response, and supporting DevOps and engineering teams with actionable insights. This is an exciting opportunity to get in on the ground floor implementing and scaling the tools, processes, and governance at HP. Responsibilities Observability Architecture & Ownership Design and implement an enterprise‑grade observability strategy spanning Datadog (metrics, logs, traces/APM, synthetics, RUM, network performance, cloud cost) and integrations with ServiceNow . Define monitoring standards, tagging conventions, dashboards, SLOs/SLIs, and alerting policies for infra and apps (on‑prem, cloud, containers). Datadog Implementation & Scale Deploy and manage Datadog agents, integrations (AWS/Azure/GCP, Kubernetes, NGINX, DBs, messaging), and service catalog coverage. Build golden dashboards, standardized monitors, and runbooks for infra components (compute, storage, network), platforms (Kubernetes), and critical apps. ServiceNow Integration & Event Management Implement and optimize Datadog → ServiceNow event routing, correlation rules, deduplication, and Incident/Problem auto‑creation with enriched context. Maintain CI relationships in ServiceNow CMDB , drive discovery mapping, and align alerts with CI ownership and support groups. Enable closed‑loop remediation using IntegrationHub , workflows, and change controls; contribute to Change Advisory Board (CAB) standards. Reliability Engineering & Operational Excellence Maintain SLOs, error budgets, and escalation policies. Reduce alert noise; drive actionable, tiered alerts. Partner with App, Infra, SecOps, and NOC teams to improve MTTR and post‑incident revie

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