Senior Observability Engineer

What this job involves:

As a Senior Observability Engineer,you'lllead the design and implementation of enterprise observability solutions across JLL's global technology estate, withprimaryfocus on Datadog.You'llarchitect AI-powered reliability solutions that deliver measurable business outcomes while serving as a recognized subject matter expert with organization-wide influence. Working across infrastructure, network, cloud, and application environments,you'lldrive visibility improvements,establishautomation standards, and enable teams to derive maximum value from observability data.

At JLL, we are collectivelyshapinga brighter way, and yourexpertisewill be instrumental in making operations smarter, faster, and more reliable.You'llprovide technical guidance and mentorship on enterprise observability practices whilemaintainingstrategic alignment between technical execution and businessobjectives. This role offers the opportunity to work withcutting-edgeAIOps and AI capabilities while solving complex technical challenges and leading transformational initiatives that define how weoperateat scale.

What your day-to-day will look like:

Strategic Leadership & Enterprise Architecture:

• Lead observability workshops and major incident response coordination across APAC, AMER, and EMEA regions,establishingbest practices and coaching engineers on operational excellence

• Architect enterprise-scale observability solutions with comprehensive reliability considerations, reviewing and influencing design decisions across multiple teams

• Drive organizational improvements in monitoring standards and practices, influencing how resources areallocatedand priorities are set

• Champion observability strategy as part of broader technology initiatives while leading transformation projects that modernize how wemonitorand operate services

Advanced Technical Implementation:

• Build, configure, and optimize Datadog capabilities across infrastructure, applications, networks, cloud platforms, logs, and digital experience monitoring with AI-firstmethodology

• Develop sophisticated dashboards, monitors, alerts, integrations, SLIs, SLOs, and service health views that enable data-driven operational decisions

• Configure and troubleshoot Datadog agents, integrations, APIs, collectors, and telemetry pipelines, applying advanced analytical approaches to resolve complex operational challenges

• Build scalable automation usingRundeck, Ansible, Python, PowerShell, APIs, and Git-based workflows that standardize observability onboarding, configuration management, self-healing remediation, and platform operations

Innovation & Business Impact:

• Establish observability-as-code practices and create reusable patterns, frameworks, and solutions for adoption across the enterprise

• Configure andoptimizeAIOps capabilities including event correlation, anomaly detection, intelligent alerting, noise reduction, and incident enrichment while integrating AI into operational workflows

• Leverage generative AI and AI-assisted engineering to enhance observability effectiveness, accelerate troubleshooting, improve root-cause analysis, and streamline automation development

• Identifyopportunities to reduce operational toil,eliminatealert fatigue,optimizetelemetry costs, and improve signal-to-noise ratio whilemaintainingcomprehensive visibility

Cross-Functional Influence:

• Integrate Datadog with enterprise platforms such as ServiceNow,Rundeck, cloud services, and CI/CD pipelines to create cohesive operational ecosystems

• Investigate and resolve sophisticated application and infrastructure performance issues using metrics, logs, traces, events, and network telemetry while collaborating with globally distributed teams

• Shape product and platform strategies through observability leadership, communicating complex technical concepts and building consensus on operational approaches

• Drive adoption of transformational changes across multiple teams, ensuring quality and consistency of operational deliverables whilemaintainingenterprise standards

Required qualifications:

• Bachelor's degree in Computer Science, Information Technology, Engineering, or related technical discipline, or equivalent combination of education and professional experience

• 5–8+ years of progressive experience in observability, monitoring, Site Reliability Engineering (SRE), infrastructure engineering, application performance monitoring, or related enterprise technology roles withdemonstratedtechnical leadership

• Deep hands-on experience implementing, configuring, and supporting Datadog in large-scale enterprise environments with proven organization-wide impact

• Expert understanding of modern observability concepts including metrics, logs, traces, telemetry, distributed tracing, alerting, service levelobjectives, and error budgets withdemonstratedability to architect solutions at enterprise scale

• Advancedexpertisewith automation and orchestration technologies such asRundeckand Ansible, with proven ability to build AI-enhanced operational solutions

• Strongproficiencyin scripting and automation using Python, PowerShell, Bash, or similar languages with experience creating enterprise automation frameworks

• Extensive experience working with REST APIs, webhooks, Git/GitHub, and CI/CD technologies withdemonstratedsuccess integrating observability into enterprise workflows

• Expert-level knowledge of major