Senior Observability Engineer

What this job involves: 

As a Senior Observability Engineer, you'll lead the design and implementation of enterprise observability solutions across JLL's global technology estate, with primary focus on Datadog. You'll architect 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'll drive visibility improvements, establish automation standards, and enable teams to derive maximum value from observability data. 

At JLL, we are collectively shaping a brighter way, and your expertise will be instrumental in making operations smarter, faster, and more reliable. You'll provide technical guidance and mentorship on enterprise observability practices while maintaining strategic alignment between technical execution and business objectives. This role offers the opportunity to work with cutting-edge AIOps and AI capabilities while solving complex technical challenges and leading transformational initiatives that define how we operate at 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, establishing best 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 are allocated and priorities are set 

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

Advanced Technical Implementation: 

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

• 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 using Rundeck, 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 and optimize AIOps 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 

• Identify opportunities to reduce operational toil, eliminate alert fatigue, optimize telemetry costs, and improve signal-to-noise ratio while maintaining comprehensive 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 while maintaining enterprise 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 with demonstrated technical 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 level objectives, and error budgets with demonstrated ability to architect solutions at enterprise scale 

• Advanced expertise with automation and orchestration technologies such as Rundeck and Ansible, with proven ability to build AI-enhanced operational solutions 

• Strong proficiency in 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 with demonstrated success integrating observability into enterprise workflows 

• Expert-level knowledge of major cloud platforms, particularly Azure and AWS; GCP experience beneficial, with demonstrated multi-cloud operational capabilities 

• Deep familiarity with Infrastructure as Code (IaC) and configuration management practices, with hands-on experience applying observability-as-code methodologies 

Preferred qualifications: 

• Proven expertise across multiple observability domains including infrastructure monitoring, APM, log analytics, network performance monitoring, cloud monitoring, and digital experience monitoring with ability to architect solutions spanning multiple domains 

• Extensive hands-on experience with AIOps capabilities such as anomaly detection, event correlation, intelligent alerting, and automated incident analysis with track record of leading AI strategy and implementation 

• Demonstrated experience with generative AI and AI-assisted engineering, particularly applications that accelerate observability, automation development, troubleshooting effectiveness, and operational efficiency 

• Proven track record leading high-visibility, cross-functional initiatives and delivering organizational value through process innovation and technology modernization 

• Relevant certifications in Datadog, cloud technologies (AWS Certified Solutions Architect, Azure Administrator, GCP Professional Cloud Architect), automation, SRE, or related disciplines 

• Exceptional communication and influencing skills with demonstrated ability to work effectively in globally distributed teams, drive transformational change, and shape strategic decisions across APAC, AMER, and EMEA time zones