About this role
The Data Pipeline team, within the MongoDB Observability organization, builds and supports scalable, highly available capabilities for transporting and transforming telemetry data across MongoDB. The team's mission is to provide a unified streaming pipeline that ingests data and processes high-throughput telemetry, enabling the migration of legacy observability solutions within MongoDB.
Built on a diskless, Kafka-compatible data streaming platform as the core streaming bus and Apache Flink for real-time transformations and delivery to storage, the platform lives on Kubernetes clusters and enables downstream consumers to access live telemetry for immediate analysis and reliable long-term retention. The main concern of this team is to provide reliable systems with high availability and replay capabilities.
About the role
We're looking for a Software Engineer 3 to help build and operate this platform. This is an individual contributor role that combines hands-on software development with systems and site reliability engineering. You'll contribute code and ship backend improvements, while also troubleshooting production issues and helping keep the services reliable.
If you enjoy both building distributed systems and understanding how they behave in production, this role offers the opportunity to work on high-volume telemetry processing with meaningful operational responsibility. You'll contribute to medium-sized technical projects and grow with support from experienced engineers.
We are looking to speak to candidates based in Dublin for our hybrid working model.
We're looking for someone who
• Enjoys working with other engineers, is approachable and curious, and is open to feedback
• Has experience with Kafka-compatible streaming platforms, Apache Flink, or comparable stream-processing technologies
• Has experience writing production backend systems in a compiled language, such as Java, Go, C, or C++
• Has experience designing and developing scalable distributed services, with an understanding of performance, availability, and failure handling
• Has worked with event-driven systems, ingestion pipelines, or high-throughput data processing
• Has hands-on experience managing the development and release lifecycle on Kubernetes clusters using GitOps tools such as Argo CD or Flux
• Has hands-on experience applying a data-driven approach to debugging and troubleshooting production systems using tools such as profilers and flame graphs to collect evidence
• Is comfortable being part of 24/7 on-call rotations
• Can explain their own technical contributions, the trade-offs they made, and the evidence used to evaluate results
• Has a strong understanding of algorithms, data structures, and time and space complexity
Nice to haves
• Experience with telemetry, metrics, logs, or observability infrastructure
• Experience using service-level indicators, objectives, or agreements to guide reliability improvements
• Experience improving monito