Listed on LSEG’s own careers site. You apply with them directly — we never stand between you and the employer.
What this role is
A senior hands-on reliability engineer will lead the establishment of SRE foundations, define observability standards, and collaborate across architecture and engineering teams to embed reliability into systems from inception. This role suits an experienced infrastructure expert comfortable with technical leadership, incident response, and mentoring while working across global teams in a financial markets organization.
Our summary, not LSEG’s wording. The full posting is on their site.
Skills this role names
- Amazon Bedrock
- Amazon Web Services (AWS)
- AWS CloudFormation
- Azure Kubernetes Service (AKS)
- Cloud Security
- Datadog
- Elastic Stack (ELK)
- Generative AI
- Grafana
- Kubernetes
- Linux
- Microsoft Azure
- OpenTelemetry
- Prometheus
- Terraform
- Vector Databases
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What they ask for
Required
- Bachelor's degree in Computer Science or related field
- 10+ years hands-on experience in SRE, Platform Engineering, Infrastructure, or related roles
- Strong Azure experience including AKS, Container Apps, Virtual Machines, VNet, and managed services
- Hands-on Kubernetes and containerization experience
- Strong Linux systems administration background
- Experience designing and operating observability platforms
- Hands-on Datadog experience with metrics, logs, APM, and alerting
- Understanding of SRE principles including SLOs, error budgets, and incident management
- Experience collaborating with architecture and engineering teams on system design
- Understanding of cloud security principles and collaboration with security teams
- Experience with cloud cost optimization strategies and tooling
- Experience integrating AI with observability stacks for proactive issue detection
Nice to have
- AWS experience or knowledge
- Multi-cloud or hybrid environment support
- Infrastructure as Code experience (Terraform, CloudFormation)
- Experience in large-scale, complex, or regulated environments
- Knowledge of vector databases and RAG architectures for SRE knowledge assistants
- Knowledge of Generative AI and LLM platforms