Backend Team Lead
About The Position
Coralogix is rebuilding the path to observability using a real-time streaming analytics pipeline that provides monitoring, visualization, and alerting capabilities without the burden of indexing.
By enabling users to define different data pipelines per use case, we provide deep Observability and Security insights, at an infinite scale, for less than half the cost.
As a team leader in Coralogix, you will lead a team of cross-disciplinary engineers. Together you’ll design, implement and operate cutting-edge product features that use and evolve our stateful streaming technology, which is based on KafkaStreams, AkkaStreams, and a lot of other distributed storage and processing technologies.
Your role will be to lead with this vision in mind, manifesting it in your leadership decisions and in the ongoing mentoring of your team.
- Our Stack:
- scala, nodejs
- Kafka Streams / Akka Streams
- Spark
- Kafka, ElasticSearch, Redis
- Kubernetes
- AWS
Responsibilities
- Lead a team of engineers to fully own large-scale products and features, from design to production, maintaining and evolving them to provide maximum value according to the evolving needs of the company
- Understand the ongoing product/technical tradeoffs and rationale, striving to create maximum alignment between the team and the company so the team engineers can constantly improve their decision making
- Mentor your team’s engineers, and facilitate their growth so they become the best engineers possible
- Enable an inclusive and balanced team culture that promotes healthy and professional discussions
Requirements
- B.Sc in Software Engineering or Computer Science
- 2+ years of experience as a team leader of software engineers in a SaaS environment
- 4+ years of experience in developing production-grade software products, preferably in a SaaS environment
- Excellent written and verbal communication skills
- Proven experience in distributed processing or streaming platforms
- Ability to understand and communicate product/technical tradeoffs that arise from different data modelling decisions