Senior Backend Engineer: Asset Sales – M-Kopa
Job Purpose
To design, build, and operate distributed C#/.NET microservices on Azure and Kubernetes—owning the full software lifecycle under a DevOps and AI-native engineering culture to deliver highly resilient, event-driven systems at scale.
Roles & Responsibilities
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Full-Lifecycle Software Ownership: Own applications and supporting infrastructure from inception to production and decommissioning, designing for idempotency, retries, and failure modes.
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Microservices & Event-Driven Development: Build scalable, distributed microservices using modern C#/.NET and event-driven messaging technologies such as Azure Service Bus, Kafka, or RabbitMQ.
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Cloud Infrastructure & Orchestration: Deploy and manage services on Azure utilizing Kubernetes and container orchestration best practices.
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Observability & Incident Operations: Leverage Grafana and Azure Application Insights to monitor production behavior, investigate incidents, and build system resilience through on-call rotations.
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AI-Native Engineering & Experimentation: Integrate AI tooling (like Claude) throughout the engineering workflow for design exploration, code generation, code review, test writing, and technical communication, continuously sharing effective workflows.
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Quality & Safety Safeguards: Maintain high reliability through automated testing, Service Level Objectives (SLOs), alerting, and adherence to the Campfire Rule of continuous code improvement.
Qualifications
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Bachelor’s degree in Computer Science, Software Engineering, or a related technical discipline (or equivalent practical experience).
Skills, Experience & Competences
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Strong grasp and professional experience in C# and .NET development.
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Proven experience building event-driven systems using message brokers such as Azure Service Bus, Kafka, or RabbitMQ.
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Solid microservices architecture experience, ideally hosted on Azure and orchestrated with Kubernetes.
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Familiarity with production observability tooling (e.g., Grafana, Azure Application Insights, logs, traces, and metrics).
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Demonstrated comfort using AI coding tools as an active part of daily engineering workflows.
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Strong DevOps mindset with proven on-call experience and ownership of production systems.
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Exceptional collaboration and communication skills within distributed, cross-time-zone teams.
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Continuous learning mindset, autonomy, curiosity, and openness to feedback.