Elastic Search

Teamware Solutions is seeking a skilled Elasticsearch Engineer to design, implement, and manage robust search and analytics solutions. You'll be crucial in building and optimizing highly scalable data indexing, search, and aggregation capabilities, enabling powerful real-time insights and enhancing our application's search functionalities.

Key Responsibilities

• Design, deploy, and manage Elasticsearch clusters for high availability, scalability, and performance.
• Develop and optimize Elasticsearch queries, aggregations, and mappings to meet complex search and analytical requirements.
• Implement and configure data ingestion pipelines using tools like Logstash, Beats, and Kafka to feed data into Elasticsearch.
• Integrate Elasticsearch with various applications and data sources, ensuring efficient data indexing and retrieval.
• Monitor Elasticsearch cluster health, performance, and resource utilization, performing proactive tuning and troubleshooting.
• Develop and maintain custom Elasticsearch plugins, scripts, or solutions as needed.
• Collaborate with development teams, data engineers, and analysts to define indexing strategies and optimize search relevance.

Qualifications

• Proven experience as an Elasticsearch Engineer or a similar role focused on search platforms.

Skills Required:

• Strong hands-on experience with Elasticsearch (versions 7.x/8.x), including cluster administration, indexing, querying, and aggregation.
• Proficiency in using Kibana for data visualization, dashboard creation, and monitoring.
• Experience with data ingestion tools such as Logstash and Beats.
• Solid understanding of RESTful APIs and integrating with Elasticsearch programmatically.
• Familiarity with database concepts and distributed systems.
• Proficiency in at least one scripting language (e.g., Python) for automation.
• Strong analytical, problem-solving, and debugging skills for complex search issues.

Preferred Skills:

• Experience with Kafka for real-time data streaming into Elasticsearch.
• Knowledge of cloud platforms (e.g., AWS, Azure, GCP) and their managed Elasticsearch services (e.g., Amazon OpenSearch Service).
• Familiarity with Elastic Stack components like APM, Security, and Machine Learning.
• Experience with performance tuning and capacity planning for large Elasticsearch clusters.