Cloud Observability Platform Engineer (Contractor)

Skills this role screens for

About the role

Cloud Observability Platform Engineer - Contract

Qube Research & Technologies (QRT) is a technology and data-driven quantitative investment manager. We build systems that directly support research, trading and engineering activity.

As our cloud estate scales, observability can no longer be treated as a set of dashboards or one-off integrations. We need standard patterns for telemetry collection, storage, alerting, service ownership, onboarding and team adoption across a large, multi-account AWS estate.

We already operate an established observability capability supporting our on-premises platforms. This role exists to help build the equivalent capability across our cloud estate. We are looking for engineers who have successfully built similar capabilities before and can apply those lessons here.

We are looking for hands-on Cloud Observability Platform Engineers to help build the foundations for cloud observability across AWS and EKS/Kubernetes.

This is a Platform Engineering role, not a dashboard-building role.

The work

What You'll Do

Success depends on making cloud observability easier to adopt than bespoke alternatives while continuously improving the platform as the cloud estate evolves.

• Help design and build the standard cloud observability patterns for AWS and EKS.
• Build telemetry collection and distribution at scale across a large, multi-account AWS estate.
• Translate AWS telemetry into patterns that integrate cleanly with our existing metrics, logs and alerting platforms.
• Deploy observability components, including patterns that avoid circular dependencies where the observability platform relies on itself to remain observable.
• Integrate cloud telemetry with inventory and service ownership data, so alerts and dashboards map back to real services and accountable owners.
• Build or integrate logs, metrics and alerting platforms, including storage, routing, retention and PagerDuty integration.
• Support retrospective deployment of observability standards across an existing cloud estate.
• Build repeatable onboarding patterns so new cloud workloads adopt observability consistently from day one.
• Build user configuration automation for alert rules, scrape targets, dashboards and standard team-consumable patterns.
• Reduce operational toil through automation, standardisation and clear platform patterns.
• Work closely with Observability, Cloud, Platform Engineering and service-owning teams to make the platform practical to adopt.

Also

Engineers Who Thrive In This Role

Technologies And Areas We Expect You To Work Across

Our current environment includes technologies such as AWS, EKS/Kubernetes, OpenTelemetry, CloudWatch, Prometheus-compatible metrics, VictoriaMetrics, VictoriaLogs, Alertmanager, Grafana, PagerDuty, infrastructure as code, CI/CD, automation, service catalogues, inventory data, Terraform, Python and Go.

Experience with VictoriaMetrics, VictoriaLogs, Alertmanager, Terraform, Python, Go or similar tooling would be useful, but strong engineers usually learn missing tools quickly.

We are more interested in engineers who understand the underlying concepts and have successfully applied them in production than engineers who have experience with one particular vendor product.

• You have previously built or significantly expanded cloud observability capabilities in production environments.
• You automate repetitive work rather than documenting manual processes.
• You ask questions before proposing solutions.
• You recognise when you do not yet understand the problem.
• You deliberately discover how systems actually work before committing to a design.
• You adapt your approach as new information becomes available.
• You like solving root causes rather than symptoms.
• You are comfortable working across infrastructure, cloud, Kubernetes, telemetry, automation and people.
• You are pragmatic about balancing engineering quality, usability and delivery.
• You can make progress without waiting for perfect requirements.
• You understand that platform adoption matters as much as technical design.
• You are comfortable owning outcomes, not just completing tickets.

Also

Why This Role Exists

We have an established observability capability supporting our on-premises platforms. As our cloud estate grows, we are building the equivalent capability across AWS.

This role exists to accelerate that work by bringing practical experience of designing, building and scaling cloud observability capabilities in production environments. We are looking for engineers who can help us avoid common pitfalls, challenge our assumptions where appropriate, and build a platform that engineering teams naturally adopt.

What Matters Most

Also

We are far more interested in

than experience with one specific vendor product.

This is a hands-on production role. You will help build the cloud observability capability that other engineering teams will rely on.

What Success Looks Like

Successful engineers in this role typically leave behind:

• Engineering judgement
• Cloud platform experience
• Observability fundamentals
• Automation
• Systems thinking
• Curiosity
• Pragmatism
• Ability to work across teams
• Ability to discover and understand unfamiliar systems
• Ability to adapt as new information becomes available
• Evidence of learning from previous production experience
• Repeatable onboarding patterns for new cloud workloads.
• Standard observability patterns that engineering teams adopt consistently.
• Reduced manual effort required to onboard and operate cloud services.
• Telemetry that is consistently linked to service ownership and operational responsibility.
• A cloud observability capability that is easier to adopt than building bespoke alternatives.

Also

Common Anti-Patterns

The challenge is not deploying monitoring tools.

The challenge is building a cloud observability capability that scales across AWS, integrates with service ownership, supports both existing and new workloads, and survives contact with real engineering teams.

• Treating observability as dashboards rather than a platform capability.
• Optimising for tools rather than outcomes.
• Waiting for perfect requirements before making progress.
• Building systems that are technically correct but difficult for teams to adopt.
• Assuming teams will use a platform simply because it exists.
• Creating manual onboarding processes that do not scale.
• Ignoring ownership, inventory and service context when building alerts or dashboards.
• Defending an initial design instead of adapting when new information becomes available.
• Building observability systems that create circular dependencies or cannot be debugged when they fail.