Software Platform Engineer (3 Days Left)

Overview

We are seeking a skilled Platform Engineer to join our team and drive the development, deployment, and supportability of our Kubernetes-based microservices platform, deployed on-premises by customers. You will build comprehensive observability, enable log and report extraction for service cases without real-time access, and optimize our overuse of Kafka by integrating Redis and batch processing. This role requires expertise in Kubernetes, Azure DevOps, C++ support, deployment sizing, and designing for reliability, availability, and serviceability (RAS).

Responsibilities

• Build Comprehensive Observability : Implement centralized metrics, logging, and tracing (e.g., Prometheus, Fluentd, OpenTelemetry) for .NET, Python, Java, C++, Kafka, and Redis, ensuring supportability in on-premises environments.
• Enable Log / Report Extraction : Design customer-facing tools (e.g., CLI scripts, Helm chart options) to collect and export logs / metrics from on-premises deployments for service cases, without real-time access.
• Optimize Kafka Usage : Audit and optimize Kafka configurations (e.g., topics, partitions, compression) to reduce metadata streaming overhead, monitored with Prometheus or Azure Monitor.
• Implement Alternatives : Integrate Redis (e.g., Azure Cache for Redis) for metadata caching / pub-sub and batch processing (e.g., Azure Data Factory, Kubernetes Jobs) for high-volume data, reducing Kafka dependency.
• Troubleshoot Customer Environments : Debug issues in on-premises customer deployments for services (C++, .NET, Python, Java), Kafka, and Redis, using exported logs and metrics.
• Enhance Product Supportability : Build Azure DevOps pipelines and installers (e.g., Helm charts) for consistent, supportable deployments, with documentation for customer support.
• Contribute to RAS : Own serviceability by building observability and diagnostic tools; support reliability / availability via Kubernetes optimization, autoscaling, and fault-tolerant designs.
• Enforce Standards : Implement and enforce structured logging (e.g., JSON with correlation IDs) and resource sizing standards via Azure DevOps pipelines.
• Optimize Deployment Sizing : Set Kubernetes resource requests / limits and autoscaling policies (e.g., HPA, VPA) for services, Kafka, Redis, and batch jobs, based on profiling.
• Evaluate Service Meshes : Assess service meshes (e.g., Linkerd) for improving microservice and data platform observability and communication.
• Support C++ Services : Assist developers in containerizing, deploying, and debugging C++ services, ensuring integration with observability, Kafka, Redis, or batch workflows.
• Automate with Azure DevOps : Build CI / CD pipelines in Azure DevOps for automated builds, tests, and deployments, integrating with AKS, Kafka, and Redis.

Qualifications

Nice-to-Haves