ELK / ESS Engineer

MetaRPO•Mclean, VA

About The Position

This role focuses on designing, implementing, and managing search and analytics solutions using Elasticsearch. Responsibilities include indexing large datasets, optimizing search queries, maintaining cluster performance, and ensuring data availability. The position requires expertise in query tuning, indexing strategy, cluster monitoring, and troubleshooting, often using the ELK stack. The engineer will be the sole person managing the requirement backlog and will have direct interaction with the client.

Requirements

Knowledge of indexing strategies for high-volume data.
Experience in designing scalable, secure, and resilient search architectures.
Ability to work independently in agile teams, collaborating with DevOps and Data Engineers.

Nice To Haves

Cluster Management: Deploy, configure, and maintain Elasticsearch clusters on-premise or in cloud environments (AWS, Azure).
Performance Optimization: Fine-tune query performance, index management, and shard allocation for large-scale data.

Responsibilities

Build and assemble interactive panels, charts, maps, and metrics using Kibana Lens to create comprehensive dashboards.
Design efficient time-series index mappings and data streams to ensure optimal data storage and retrieval.
Utilize aggregations, date histograms, and filters (KQL) to analyze large datasets and ensure fast dashboard response times.
Set up threshold-based alerts (Watcher) and monitor system health to provide actionable insights.
Tune dashboard panels for performance, implementing data retention policies (ILM) to maintain efficiency.
Deploy, configure, and maintain Elasticsearch clusters on-premise or in cloud environments (AWS, Azure).
Fine-tune query performance, index management, and shard allocation for large-scale data.
Develop pipelines for indexing data from various sources using Logstash or ingestion APIs.
Monitor cluster health, maintain security protocols, and ensure data integrity.
Perform root cause analysis on performance bottlenecks and cluster failures.
Collaborate with software engineers to implement search features and improve user experiences.
Troubleshoot and resolve issues related to Elasticsearch performance, data integrity, and availability.
Work independently in agile teams, collaborating with DevOps and Data Engineers.