Senior Software Engineer - Confluent

Introduction

At IBM Software, we turn client challenges into real solutions. We build the world’s leading AI-powered, cloud-native products that help shape the future of business and society. Through a deep legacy of innovation, IBMers gain countless opportunities to learn, grow, and make an impact on a global scale.

Working in Software means joining a team driven by curiosity and collaboration. You’ll work with a wide range of technologies, partners, and industries to design, develop, and deliver solutions that enable digital transformation. With a culture that champions innovation, growth, and continuous learning, IBM Software puts you at the center of IBM’s product and technology ecosystem. Here, you’ll find the tools and opportunities to advance your career while building software that helps change the world.

Your Role And Responsibilities

The Observability Metrics Platform team creates and operates the system that collects, stores, and serves metrics for Confluent Cloud. These metrics support the customer-facing Metrics API, usage and billing signals, internal alerting and dashboards, and integrations that deliver metrics into customers’ own observability environments. The team runs Apache Druid, including the ingestion and pre-processing pipeline as well as the query services behind it, across AWS, GCP, and Azure, while meeting strict goals for freshness, query latency, and availability.

As a Senior Software Engineer, you will design and ship features across this technology stack, ensuring it stays fast and reliable as data volumes increase. You’ll also collaborate with the Confluent product teams that rely on this platform to help understand, operate, and improve their services. Your work directly enhances how Confluent and its customers assess the health of their data infrastructure.

What You Will Do

• Design, build and operate high-throughput, low-latency services for metrics ingestion, storage and querying in a multi-tenant, multi-cloud environment
• Own projects end to end: convert unclear requirements into defined milestones and deliver production-ready changes with tests, dashboards and alerts
• Enhance reliability and performance across the metrics pipeline and Druid clusters, including ingestion lag, query latency, capacity, upgrades and rollouts
• Investigate and resolve difficult production problems across microservices, Kubernetes, and data stores, participate in on-call, and drive RCAs through to lasting follow-up fixes
• Raise the technical bar through design documents, rigorous code reviews and mentoring of less experienced engineers
• Work with product, platform and security teams to onboard new signals, define SLOs, and balance reliability, cost, and customer impact
• Minimize operational effort by improving runbooks, automation, and infrastructure as code
  
  

Preferred Education

Master's Degree

Required Technical And Professional Expertise

• 3 to 5 years of professional software engineering experience building and running backend or data-intensive systems in production
• Strong programming and algorithmic skills in Java
• Sound understanding of distributed systems: partitioning, replication, failure handling and performance tuning
• Experience designing, deploying and operating services on Kubernetes in a public cloud (AWS, Azure or GCP)
• Practical production experience: monitoring, debugging, incident response and on-call
• Strong engineering practices: automated testing, code review, design documents and clear written communication
• Capability to take ownership of work end to end and collaborate effectively across teams
  
  

Preferred Technical And Professional Experience

• Experience with observability concepts and tools: metrics, traces and logs, such as OpenTelemetry, Prometheus, Grafana or Datadog
• Experience with real-time analytics or time-series stores (Apache Druid, Pinot, ClickHouse) or streaming systems including Apache Kafka or Flink
• Experience improving ingestion and query performance, or planning capacity, for large datasets
• Experience with Terraform or equivalent infrastructure-as-code tooling
• Experience with migrations or multi-region rollouts, such as moving workloads from legacy systems to new architectures