Real-Time Distributed Systems Engineer, Data Platform

Cloudflare; Atlanta (GA); On-site; USD 150,000 - 231,000; Full time

Benefits offered by this job

Equity

Health insurance

401(k) matching

Paid time off

Job summary

Cloudflare is seeking an experienced Distributed Systems Engineer to join the DATA Organisation. You will contribute to the data lifecycle, from ingestion to storage, building high-performance, scalable systems that power logs and analytics for Cloudflare’s global customer base.

You’ll work on Go-based pipelines, data delivery, analytics, and customer-facing data products, with exposure to Kafka, Flink, Kubernetes, and extensive observability tooling, while collaborating with cross-functional

Qualifications

• 3+ years of experience in software development covering distributed systems and data pipelines.
• Strong programming skills (Go preferred) with high-throughput systems expertise.
• Hands-on experience with observability stacks and high-cardinality metrics.

Responsibilities

• Design, build, and operate a robust logging platform for customer destinations.
• Develop and maintain data connectors and integrations for log-shipping products.
• Create and manage auditing logs and ensure compliance and performance standards.
• Scale data delivery pipelines to handle massive data volumes with low latency.
• Collaborate with product and engineering teams to define requirements for new logging platform integrations.
• Maintain on-call readiness and monitor the log delivery platform.

Skills

Distributed systems

Go programming

Data pipelines

SQL

Observability

Tools

Prometheus

Grafana

Kafka

Flink

Kubernetes

Docker

Terraform

Job description

Cloudflare is seeking an experienced Distributed Systems Engineer to join the DATA Organisation. You will contribute to the data lifecycle, from ingestion to storage, building high-performance, scalable systems that power logs and analytics for Cloudflare’s global customer base.

You’ll work on Go-based pipelines, data delivery, analytics, and customer-facing data products, with exposure to Kafka, Flink, Kubernetes, and extensive observability tooling, while collaborating with cross-functional