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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff System Reliability Engineer - **Company:** SIEMENS, S.A. - **Location:** Valencia, Spain - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Cloud Computing, Computer Programming, Continuous Integration, Data Stores, Disaster Recovery, Distributed Systems, Elasticsearch, Graph Database, Identity and Access Management, Python (Programming Language), Load Testing, Scrum Methodology, Reliability Engineering, Prometheus, Systems Integration, TypeScript, Grafana, Software Security, State Machines, Reliability of Systems, Servicebus, Event Driven Architecture, Kubernetes, AWS Fargate, Apache Kafka, Functional Programming, Cloudwatch, Software Coding, Amazon Simple Queue Service (SQS), Terraform, OpenSearch, Golang - **Published:** October 31, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=12fa0d5d2ea17c12 ## About the Role * Experience: 10+ years in software, systems, or site reliability engineering, including owning critical distributed services in production and acting as the technical lead for reliability. * Reliability engineering: Defining SLIs, SLOs, and error budgets, and turning them into engineering and release decisions. * Distributed systems: Asynchronous and event-driven architectures, retries, idempotency, backpressure, eventual consistency, and failure containment. * Incident management: Leading high-severity incidents, root-cause analysis, and blameless post-incident reviews through to completed corrective actions. * Observability and performance: Logs, metrics, traces, and profiling with OpenTelemetry, CloudWatch, Prometheus, Grafana, or similar, plus load testing and capacity planning. * Cloud and delivery: AWS services such as ECS or Fargate, Lambda, SQS, EventBridge, Step Functions, and IAM; Terraform or CDK; CI/CD with progressive delivery and rollback. * Programming: Strong coding skills in Go, Java, Python, TypeScript, or a comparable language; able to diagnose unfamiliar systems and implement durable fixes. * Communication: Influencing engineers and stakeholders without formal authority, explaining reliability risk and trade-offs clearly, and excellent English., * Product security domain: Operating SBOM, CBOM, VEX, vulnerability-management, or software-composition-analysis platforms. * Standards: Familiarity with CycloneDX, SPDX, PURL, CPE, SLSA, the EU Cyber Resilience Act, NIS2, or IEC 62443. * Search, graph, and messaging: Operating graph databases, OpenSearch, Elasticsearch, Kafka, or workflow engines such as Temporal or Camunda at scale. * AI-assisted operations: Applying AI to diagnostics, anomaly investigation, or runbook automation with clear verification and human accountability. * Certifications: AWS, Kubernetes, or comparable cloud and reliability credentials. ## Description * Reliability at scale: Keep many services, APIs, data stores, and integrations dependable as load, data volume, and the number of teams grow. * Measurable reliability: Turn business-critical user journeys into SLIs, SLOs, and error budgets that actually shape priorities and release decisions. * Distributed and asynchronous systems: Keep event-driven, long-running workflows correct and recoverable under retries, duplicates, overload, and partial failure. * High-trust data under failure: Prevent outages, degraded dependencies, or failed migrations from silently corrupting, losing, or staling product-security data. * Dependencies we do not control: Contain the impact of degraded enterprise systems and external integrations so core capabilities stay available. * Recovery that is proven: Turn backup, restore, and disaster-recovery plans into tested, measurable capabilities rather than documentation. * Change without disruption: Release, migrate, and re-architect continuously through progressive rollout, automated verification, and safe rollback. * Reliability without a central gate: Shift reliability left into design, refinement, and the Definition of Done so every team owns production readiness., * Own the reliability strategy and define SLIs, SLOs, and error budgets for critical user journeys and services. * Own operational readiness from a reliability perspective, covering availability, performance, recoverability, and secure operations. * Design, build, and evolve the observability model, including telemetry standards, dashboards, alerts, and synthetic checks, and the shared reliability tooling used across teams. * Review architectures, designs, and code hands-on for partial failure, overload, idempotency, backpressure, and safe degradation, and implement critical reliability improvements yourself. * Engineer safe delivery through progressive rollout, automated verification, rollback, and protection against risky configuration or schema changes. * Own capacity and performance engineering, and prove backup, restore, and disaster recovery through regular tests and controlled failure experiments. * Lead high-severity incident response, run blameless post-incident reviews, and drive root-cause fixes that remove whole classes of failure. * Reduce operational toil through automation, self-service capabilities, and runbooks that stay accurate under pressure, while preserving least privilege and auditability. * Mentor engineers technically in production ownership and promote reliability as a shared Scrum Team responsibility.