Senior Elasticsearch Engineer - ELK/EFK

6 - 10 years

8 - 12 Lacs

Posted:None| Platform: Naukri logo

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Job Type

Full Time

Job Description

Experience Range: 6 to 10years
Location: Bangalore
Job Summary:
We are looking for a skilled Elasticsearch Engineer with expertise in designing, implementing, and managing ELK (Elasticsearch, Logstash, Kibana) and EFK (Elasticsearch, Fluentd, Kibana) stacks. The ideal candidate will be responsible for creating and maintaining scalable, high-performance search and analytics solutions that utilize Elasticsearch along with centralized logging and monitoring capabilities via Logstash, Fluentd, and Kibana. This role involves collaborating with cross-functional teams to develop real-time data processing and visualization solutions.
Required Skills:
  • Elasticsearch Expertise: Strong experience with Elasticsearch, including advanced features like aggregations, search relevancy, and multi-index management.
  • ELK/EFK Stack Experience: Deep understanding of the ELK/EFK stack (Elasticsearch, Logstash/Fluentd, Kibana) and experience designing log aggregation and visualization solutions.
  • Data Ingestion Tools: Proficient in setting up data ingestion pipelines using Logstash or Fluentd.
  • Scripting & Programming: Experience with scripting languages (Python, Shell) or development languages like Java, Node. js for automation and integration tasks.
  • System Monitoring & Logging: Experience with logging frameworks (e. g. , Fluentd, Logstash) and monitoring tools such as Grafana, Prometheus.
  • Cloud Platforms: Knowledge of deploying and managing Elasticsearch on cloud platforms (AWS, GCP, Azure), including using managed services like AWS Elasticsearch Service or OpenSearch.
  • DevOps Practices: Familiarity with containerization and orchestration tools like Docker and Kubernetes, including EFK stack deployment in Kubernetes environments.
  • Security: Understanding of Elasticsearch security configurations, including TLS, authentication, and user role management.
Preferred Qualifications:
  • Experience working with large-scale, high availability distributed systems.
  • Knowledge of message queueing systems like Kafka, RabbitMQ for streaming data into Elasticsearch.
  • Familiarity with infrastructure-as-code tools like Terraform, Ansible, or Helm for provisioning and managing ELK/EFK stacks.
  • Experience with other log aggregation and monitoring solutions such as Splunk or Datadog.

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