Join our team as a Full Stack Engineer, focusing on developing and engineering comprehensive system features from user experience to backend code. You will leverage your development expertise to deliver innovative solutions that enhance client services. Embracing a cloud-first, agile mindset, you will utilize cutting-edge technologies to tackle complex business challenges.

As an Elastic Stack Engineer, you will be instrumental in designing, implementing, and supporting enterprise search, log analytics, and observability solutions. Your daily tasks will involve data ingestion, optimizing search performance, creating insightful dashboards, monitoring systems, and providing production support within cloud-native environments.

Key responsibilities include acting as a Subject Matter Expert for Elasticsearch, Kibana, and Logstash. You will manage Elasticsearch indices, mappings, and ILM policies, while building and refining complex queries and aggregations. Developing Logstash pipelines for data transformation and integrating various data sources are also core functions. Furthermore, you will create compelling Kibana dashboards and visualizations, configure security settings, and deploy/manage Elastic Stack components using containerization technologies.

We are seeking a skilled engineer with a minimum of 3 years of dedicated experience in Elastic Stack engineering. Essential skills include deep expertise in Elasticsearch, Kibana, and Logstash, alongside strong proficiency in Elasticsearch mappings, Query DSL, aggregations, and ILM. Hands-on experience with Logstash pipelines, including Grok and Dissect, is crucial. The role also requires experience in building Kibana dashboards and visualizations, as well as familiarity with tools like Filebeat, Metricbeat, and Elastic Agent.

Experience deploying and operating the Elastic Stack on Docker and Kubernetes platforms is a must. A solid understanding of JSON, regular expressions, structured logging, and data ingestion concepts is expected. Scripting abilities in Bash or Python, along with experience in Git, CI/CD pipelines, and configuration management, are also required. The ability to diagnose cluster performance, ingestion bottlenecks, and search latency, coupled with experience supporting production environments and incident resolution, will set candidates apart. A total experience of 5 years is preferred.