Site Reliability Engineer; SRE) Thomson Reuters · Site Reliability –ago

Thomson Reuters is strengthening its Site Reliability Engineering capability to help engineering and operations teams build, operate, and improve reliable production services.

The
Site Reliability Engineer
will support the tools, processes, and operational practices that help teams detect, investigate, respond to, and prevent production reliability issues. You will work with observability platforms, operational documentation, automation, deployment information, and AI-enabled tools to improve the quality and availability of context used during day-to-day operations and incidents.

This is a hands-on engineering role for someone who enjoys learning how complex systems work, improving operational readiness, and contributing practical solutions to production challenges. You will work closely with experienced SREs, Product Engineering teams, platform teams, and operations partners to maintain reliable services and reduce operational toil.

Rather than expecting you to know every architecture on day one, this role will help you build familiarity across products and platforms through maintained documentation, dashboards, runbooks, telemetry, deployment data, and operational tooling. When you identify gaps in that context, you will help improve the systems and processes that keep it current.

You will also use AI-enabled engineering and investigation tools responsibly to accelerate analysis, documentation, and operational workflows. You will apply technical judgment, validate outputs, and elevate when additional expertise or review is needed.

• Support and maintain SRE operational tooling, including dashboards, alerts, runbooks, service documentation, telemetry baselines, deployment visibility, and dependency information.

• Use observability tools—including logs, metrics, traces, dashboards, and alerts—to investigate service-health issues, identify trends, and support incident response.

• Participate in incident response by gathering relevant context, reviewing recent changes, following established runbooks, documenting findings, and helping coordinate technical follow-up actions.

• Execute approved runbooks and mitigation procedures within established escalation, change-management, and decision-making processes.

• Clearly document facts, observations, hypotheses, actions, and open questions during incidents, handoffs, and operational reviews.

• Help improve the accuracy, completeness, and freshness of operational context used by engineering and operations teams during incidents and routine production support.

• Contribute to automation and integration work that keeps operational information current, such as CI/CD notifications, deployment telemetry, change-correlation data, service ownership records, and monitoring configuration.

• Review and validate operational artifacts, including runbooks, diagrams, dashboards, alerts, and AI-generated documentation, with guidance from senior engineers and service owners.

• Assist with root-cause analysis, post-incident reviews, and follow-up work by identifying gaps in monitoring, documentation, automation, instrumentation, or operational processes.

• Treat missing runbooks, outdated documentation, incomplete telemetry, and unclear service ownership as improvement opportunities; partner with the appropriate teams to help resolve those gaps.

• Contribute to service-health and error-reduction initiatives using available SLO, error budget, incident, alerting, and operational data.

• Partner with Product Engineering and platform teams to identify reliability and observability improvements, including monitoring gaps, alert quality, deployment visibility, capacity concerns, and failure-mode coverage.

• Contribute directly to code, scripts, infrastructure configuration, dashboards, alerts, automation, and documentation that improve service reliability and reduce manual operational work.

• Use AI-enabled coding and investigation tools to accelerate log review, documentation updates, runbook drafting, incident summarization, and hypothesis generation, while validating results before relying on them.

• Provide actionable feedback when AI-enabled operational tools produce incomplete, inaccurate, or insufficiently supported outputs.

• Participate in design reviews, sprint planning, and operational-readiness discussions, helping ensure reliability and observability considerations are addressed before production deployment.

• Support blameless post-incident reviews focused on learning, systemic improvement, and…