Principal Go Engineer - Remote, Distributed Systems

HeadHR; Województwo kujawsko-pomorskie; Hybrid; PLN 380,000 - 520,000; Full time

Benefits offered by this job

Remote work in European hours

Job summary

HeadHR is seeking a Principal Go Engineer to lead the design of a distributed data and analytics platform. You will own architecture, performance, and key components, working on large production systems that process network data on Kubernetes, in public cloud and on-premises.

You will design scalable Go backend services, build Kafka-based data pipelines, and guide architectural decisions for scalability, consistency, and disaster recovery, while optimizing CPU and memory usage and driving

Qualifications

• 10+ years of development experience with 5+ years in Go and distributed systems.
• Hands-on ownership of architecture for high-availability platforms.
• Strong Go performance optimization, concurrency, memory profiling.

Responsibilities

• Design scalable Go backend services and high-performance data pipelines.
• Build Kafka-based event-driven architecture and define data flows.
• Make architectural decisions on scalability, consistency, and disaster recovery.
• Optimize Go at the CPU, memory, and concurrency levels.
• Raise standards in code quality, security, and observability.
• Lead cross-team technical initiatives and mentor engineers.
• Collaborate with DevOps/Product teams and resolve production incidents.

Skills

Go programming

Distributed systems

Microservices

Kafka

PostgreSQL

Docker

Kubernetes

Observability

Troubleshooting

Architectural decisions

English fluency

Tools

Druid

gRPC

Protobuf

Terraform

Helm

Ansible

AWS

GCP

Azure

Job description

HeadHR is seeking a Principal Go Engineer to lead the design of a distributed data and analytics platform. You will own architecture, performance, and key components, working on large production systems that process network data on Kubernetes, in public cloud and on-premises.

You will design scalable Go backend services, build Kafka-based data pipelines, and guide architectural decisions for scalability, consistency, and disaster recovery, while optimizing CPU and memory usage and driving