Who we are
Headquartered in Singapore, SATS Ltd. is one of the world’s largest providers of air cargo handling services and Asia’s leading airline caterer. SATS Gateway Services provides airfreight and ground handling services including passenger services, ramp and baggage handling, aviation security services, aircraft cleaning and aviation laundry. SATS Food Solutions serves airlines, institutions, and operates central kitchens with large-scale food production and distribution capabilities for a wide range of cuisines. SATS is present in the Asia-Pacific, the Americas, Europe, the Middle East and Africa, powering an interconnected world of trade, travel and taste. Following the acquisition of Worldwide Flight Services (WFS) in 2023, the combined SATS and WFS network operates over 215 stations in 27 countries. These cover trade routes responsible for more than 50% of global air cargo volume. SATS has been listed on the Singapore Exchange since May 2000. For more information, please visit www.sats.com.sg
The Role
The Cloud Engineering Manager is a hands-on engineering leader responsible for the day-to-day build, operation, and continuous improvement of the global cloud platform estate across Microsoft Azure (primary), Amazon Web Services, and Google Cloud Platform. Reporting to the VP, Global Cloud and Enterprise Architecture, the role holder turns architectural standards and platform strategy into running, secure, and cost-transparent platform services that every workload, application, and data service in the enterprise builds upon.
This role sits at the center of the organization’s three-year transformation from fragmented regional technology operations into a unified Global Enterprise Architecture & Infrastructure organization. Working through the Stabilize, Standardize, and Innovate phases, the Cloud Platform Manager delivers the landing zones, guardrails, reference pipelines, and reusable infrastructure modules that give regional teams and product groups a consistent, compliant path to production.
Two capabilities distinguish this role from a conventional cloud operations position. First, the role holder is expected to automate relentlessly, applying AI wherever it is applicable: AI-assisted infrastructure engineering, AIOps-driven detection and remediation, and agent-based automation of routine platform work. Manual, repetitive effort is treated as a defect to be engineered out. Second, the role holder builds and operates the infrastructure that hosts the organization’s enterprise AI workloads, including GPU compute, managed AI services, model serving, and the secure landing zones on which the company’s in-house AI platforms run. The platform team owns this infrastructure layer and partners closely with the Data & AI function, which owns the application layer.
The successful candidate combines deep, current, hands-on engineering skill across all three major cloud platforms with the discipline to run production services and the leadership ability to develop a globally distributed team of cloud engineers.
Key Responsibilities
Cloud Platform Engineering & Operations
• Build, operate, and continuously improve the enterprise landing zones, subscriptions, accounts, and projects across Azure (primary), AWS, and GCP, including secure defaults, platform images, network integration points, and managed platform services
• Deliver and maintain standard delivery pathways, reference pipelines, reusable infrastructure-as-code modules, and environment automation so that builds are consistent, compliant, and repeatable across the enterprise
• Operate the platform service catalog with clear ownership, documented service level objectives, and a managed intake process for new platform capabilities
• Support the consolidation and modernization of on-premises VMware workloads as the organization moves to a cloud-first operating model
• Execute multi-cloud rationalization decisions set by the VP, keeping AWS and GCP footprints focused on strategic workloads while unifying identity and policy across all platforms
Automation & AI-Enabled Engineering
• Set and enforce an automation-first standard: infrastructure as code, GitOps, and pipeline-driven change as the default for every platform action, with manual changes treated as exceptions requiring justification
• Apply AI across the engineering lifecycle where applicable, including AI-assisted authoring of infrastructure code and policy, AI-supported code review, and agent-based execution of routine operational runbooks
• Build AIOps capability into platform operations: anomaly detection, intelligent alert correlation and enrichment, and automated remediation for the most frequent fault classes
• Measure and report automation coverage and toil reduction, with a visible backlog of manual work targeted for elimination each quarter
AI Workload Infrastructure
• Design, build, and operate the infrastructure that hosts enterprise AI workloads, including GPU and accelerated compute, managed AI services, model serving and inference endpoints, and vector and retrieval data services
• Deliver secure, isolated AI landing zones with private networking, workload identity, content and data controls, and cost transparency built in from the start
• Own capacity planning, quota management, and consumption and token cost reporting for AI infrastructure as adoption scales globally
• Partner with the Data & AI function on platform requirements, holding clear ownership of the infrastructure layer while the application layer remains with Data & AI
Reliability, Observability & Resilience
• Define, instrument, and report service level objectives and error budgets for all platform services, integrated with the global ITSM framework
• Operate the observability stack for platform services, covering metrics, logging, and tracing, with alert policy and event routing that reach the right teams the first time
• Own recovery playbooks, RTO and RPO evidence, and regular drill exercises for platform services, with proven recovery for tier-one dependencies
Security, Identity & Compliance
• Implement guardrails by default through policy-as-code, secure baselines, and standard delivery pathways, in partnership with Global Cybersecurity
• Manage workload identities, privileged access controls, and Conditional Access alignment across cloud platforms using Microsoft Entra ID
• Maintain compliance evidence for platform services against data protection requirements (GDPR, CCPA) and industry standards (PCI-DSS, SOC 2)
Cost Management & FinOps Execution
• Enforce tagging policy and cost allocation data quality across all cloud accounts and subscriptions
• Execute the optimization backlog, including rightsizing, scheduling, storage tiering, and commitment utilization across reservations, savings plans, and committed spend agreements
• Provide accurate consumption and forecast data to the FinOps function and business unit reporting
Team Leadership & Vendor Management
• Lead, develop, and retain a globally distributed team of cloud engineers across multiple regions and time zones
• Set engineering standards, run structured code and design review, and build a culture of operational discipline and continuous learning
• Manage day-to-day delivery from cloud service providers and engineering partners, holding vendors to committed outcomes and documented service levels
Job Requirements
(minimum acceptable proficiency for this job)
Educational Qualifications:
• Bachelor’s degree in Computer Science, Engineering, or equivalent experience.
• Relevant professional certifications highly valued: Azure Solutions Architect Expert, AWS Certified Solutions Architect Professional, Google Professional Cloud Architect, Certified Kubernetes Administrator (CKA), HashiCorp Certified: Terraform Associate, FinOps Certified Practitioner
Relevant Skills and Experience:
• 10+ years of progressive cloud and infrastructure engineering experience, with at least three years leading engineering teams and direct accountability for production platform services
• Multi-Cloud Platforms: Expert-level, current hands-on knowledge of Azure, including compute, networking, identity, security, and data services, with substantial delivery experience on both AWS and GCP. This role requires genuine build and run experience on all three platforms, not deep knowledge of one and awareness of the others
• Platform Engineering: Deep expertise in infrastructure as code (Terraform, Bicep), GitOps and pipeline-driven delivery (GitHub Actions, ArgoCD, Spacelift, or equivalent), landing zone design, and self-service provisioning at enterprise scale
• Automation & AI-Enabled Operations: Demonstrated record of automating infrastructure operations at scale, including practical production use of AI in engineering work such as AI-assisted infrastructure coding, AIOps platforms, and LLM-based agents for operational tasks, with measurable reduction in manual effort
• AI Workload Infrastructure: Hands-on experience building and running infrastructure for AI and machine learning workloads, including GPU and accelerated compute, managed AI services (Azure OpenAI Service, Amazon Bedrock, Google Vertex AI, or equivalent), model serving and inference, vector and retrieval data services, and the networking, identity, and cost controls specific to AI platforms
• Containers & Orchestration: Strong production experience with Kubernetes (AKS, EKS, or GKE), container security, and workload deployment patterns
• Identity & Security: Working depth in Microsoft Entra ID, workload identities, privileged access management, Conditional Access, zero-trust principles, and policy-as-code frameworks
• Observability & SRE: Practical experience with observability platforms (Grafana, Prometheus, Azure Monitor, CloudWatch, or equivalent), SLO and error budget management, and site reliability engineering practice
• FinOps: Working knowledge of cloud cost management tooling, tagging strategy, commitment management across reservations and savings plans, and consumption reporting
• Hybrid Estate: Experience migrating and operating workloads across VMware and on-premises environments in transition to cloud
• Global Delivery: Proven experience leading distributed engineering teams across multiple time zones and cultural contexts; experience across EMEA, APAC, and the Americas preferred
• Sector: Aviation, transportation, logistics, or field operations sector experience preferred, with an understanding of operational technology environments and 24/7/365 operational requirements
Personal Characteristics & Behaviours
• Automation-First Mindset: Instinctively treats repeated manual work as an engineering defect and builds the tooling to remove it, rather than absorbing it into the team’s routine
• Hands-On Technical Credibility: Stays close to the code and the platforms; earns the respect of senior engineers by contributing directly, not only by directing
• Delivery Discipline: Sets clear standards, measures against them, and holds self, team, and vendors accountable for committed outcomes
• Clear Communication: Explains technical decisions in plain language to both engineers and business stakeholders, and escalates early with facts
• Global Collaboration: Works effectively across regions, time zones, and cultures, sharing knowledge openly and building consistency without heavy process
Travel Requirements
Some international travel will be required
Core Values
We have 5 core People Values at SATS.
Safety: Safety – its ingrained in our DNA
Customer Focus: Every smile is a win for us
Respect: We strive to listen, understand and respect.
Excellence: Excellence isn’t just a goal, it’s the SATS way.
Teamwork: We don’t just collaborate. We amplify each other’s strengths.