Observability - Go Engineer, Intake Services Team

About the role 

The Observability team is developing and providing solutions to application developers and engineers that run infrastructure and services supporting these applications. Elasticsearch is an efficient datastore for logs, metrics, application traces and profiling data. The Observability team builds and maintains solutions that make getting insights from this data turnkey and efficient, such as our APM, Infrastructure Monitoring, Logs and Universal Profiling solutions. When developing these solutions, we think about the problem end-to-end: how do we automatically collect data from common data sources, how do we store it efficiently in Elasticsearch, how do we present this information to the user, what actions do we take on the insights from the data? All of these aspects are important in bringing a turnkey solution to the market. 

As a Go Engineer in the Observability Intake Services team, you will be part of a team developing high quality observability products that are essential to the ingest flow of the Elastic Observability solution. The team’s main focus is on building and maintaining backend intake service components for cloud managed and on-prem deployments, and owning operational stability through extensive monitoring and SLOs. We have a focus on quality, performance, reliability, security and testability, as well as automation and continuous improvements. 

We build components and services that receive and validate signals from various edge collectors, such as Elastic APM, OpenTelemetry SDKs, as well as profiling agents, and transform them into Elasticsearch documents. The intake service components control the amount of data flowing into Elasticsearch and can buffer data temporarily without adding overhead to the edge applications. 

We appreciate community contributions, and as such, engaging with the open source community is also an important part of this role.

You’ll be collaborating with immediate team colleagues and across team boundaries. The team is diverse and distributed across the world, and collaborates on a daily basis over GitHub, Zoom, and Slack. 

What you will be doing:

• Design and implement new features for the observability intake services in Go.
• Maintain, update and improve existing code and dependencies while ensuring automated test coverage.
• Create and improve the development infrastructure using modern tooling such as Tilt and Terraform.
• Build a strong backend solution for the OpenTelemetry data flow.
• Design data formats for optimized data storage and query performance in Elasticsearch.
• Provide Pull Request reviews and guidance to your peers.
• Build SLIs and SLOs and participate in an on-call rotation for the services in the team’s ownership.
• Proactively participate in team conversations and contribute to a positive team culture. 
• Collaborate with other parts of the company on cross team features and topics.
• Collaborate with the support team on customer requests, and engage with the community via pull requests, issues and discuss forums. 

What you bring along: 

• A positive attitude and a curious mindset.
• Experience with Go, with strong analytical, debugging, and troubleshooting skills.
• Interest in going deep on advanced topics such as memory management, benchmarking or profiling.
• Experience and knowledge in the observability space (developing monitoring products, working with metrics and sampling strategies, etc).
• Hands-on experience with Docker and Kubernetes. 
• Ability to work independently in a globally distributed team, while ensuring to collaborate and share knowledge with your colleagues.

Bonus points

• Experience with or interest in OpenTelemetry
• Experience developing high throughput, low latency services.
• Experience working with stream processing platforms such as Apache Kafka or cloud provider managed queues.
• Understanding how to design and implement SLOs
• Experience working with various cloud service providers 
• Knowledge of Elasticsearch and/or Kibana.
• Experience participating in technical decision-making across different engineering teams

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