We are seeking an experienced Go Backend Engineer to join the team responsible for transforming the platform into a multi-tenant, event-driven architecture. The ideal candidate has deep hands-on experience with Go microservices, distributed data flows, and Kubernetes-based deployments in cloud environments.

This role involves designing and developing backend services, integrating real-time messaging pipelines, ensuring high observability standards, and collaborating with platform, data, and DevOps teams.

Technical Requirements

• 5+ years of backend software development experience.
• Strong professional experience with Go (Golang).
• Practical experience designing or maintaining microservices.
• Experience with event-driven messaging systems such as Apache Pulsar (preferred), Kafka, or NATS.
• Hands-on knowledge of Kubernetes, including deployments, scaling, and configuration.
• Familiarity with cloud platforms (AWS, GCP, or Azure).

Cloud & Deployment

• Hands-on knowledge of Kubernetes, including deployments, scaling, and configuration.
• Practical experience with Kubernetes operators, CRDs, Helm.
• Understanding of cloud CI/CD systems and delivery pipelines (GitHub Actions).

Data

• Experience with PostgreSQL and Redis.
• Understanding of Data Engineering concepts, including ETL/ELT and streaming workflows.

Nice to Have

• Experience with C/C++ (networking, concurrency, high-performance systems).
• Experience with Python, especially for data workflows.
• Experience with Ruby on Rails ecosystem.
• Experience designing and building multi-tenant data architectures.
• Experience implementing observability stacks (Prometheus, Grafana, OpenTelemetry).

Responsibilities

• Design, implement, and maintain Go microservices powering the data platform.
• Integrate services with Apache Pulsar (or equivalent streaming tools) to support real-time data distribution.
• Ensure observability and reliability through logging, metrics, and tracing.
• Deploy and maintain services in Kubernetes environments across cloud platforms.
• Collaborate with Data Engineering, Platform, and DevOps teams during architecture and implementation stages.
• Participate in technical discussions, code reviews, and performance optimizations.

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