CI Poly Required)

Everfox•USA_VA_Onsite, VA

5d•$138,600 - $183,200•Onsite

About The Position

We are seeking an experienced Senior Elasticsearch Engineer to design, develop, optimize, and maintain enterprise-scale search platforms powered by the Elastic Stack. The ideal candidate will have deep expertise in Elasticsearch architecture, indexing strategies, query optimization, cluster administration, and observability solutions. You will collaborate with software engineers, data engineers, and DevOps teams to build highly available, scalable search solutions that support mission-critical applications.

Requirements

An active (currently in use) – TS/SCI with CI Polygraph
A bachelor’s degree in computer science, Information Systems, Cyber Security, or equivalent education and work experience.
At least one DoD Approved 8570 Baseline Certification at IAT level II (2) or higher. (These are required only for DoD professional services roles.)
The ability to obtain the appropriate work authorizations required under U.S. ITAR or EAR regulations from the applicable U.S. government agency.
Deep expertise in Elasticsearch architecture, indexing strategies, query optimization, cluster administration, and observability solutions.
Experience with Elastic Stack components including: Elasticsearch, Kibana, Logstash, Beats, Elastic Agent
Experience with Index Lifecycle Management (ILM), snapshots, backups, and disaster recovery strategies.
Experience designing mappings, analyzers, tokenizers, and ingest pipelines.
Experience building dashboards and visualizations using Kibana.
Experience integrating Elasticsearch APIs into applications.
Experience with cluster upgrades, scaling, and capacity planning.
Experience implementing security using Elastic Security, RBAC, TLS, and authentication mechanisms.
Experience automating deployments using Infrastructure as Code (Terraform, Ansible, Helm, or similar).
Experience supporting production incidents and performing root cause analysis
Experience with Cluster Deployment: Installing, configuring, and scaling Elasticsearch nodes and indices.
Experience with Log & Metric Monitoring: Integrating database audit logs, alert logs, and OS metrics into the ELK Stack (Elasticsearch, Logstash, Kibana).
Experience with Search Optimization: Mapping definitions, query optimization, and shard/replica management for fast text-based analytics.
Experience with Troubleshooting: Monitoring cluster health, JVM heap tuning, and resolving node dropouts or lag issues

Nice To Haves

Vector & Semantic Search: Experience with dense vector fields, embeddings, and hybrid ranking (Reciprocal Rank Fusion) using the Elasticsearch Relevance Engine.
Machine Learning Integration: Familiarity with Learning to Rank (LTR), integrating third-party transformer models
Cloud & Orchestration: Hands-on practice with Elastic Cloud (ECE/ESS), Kubernetes operators, or AWS-managed deployments
Advanced Operations: Skills in Cross-Cluster Replication (CCR), cross-cluster search, and custom Painless scripting for complex data streams

Responsibilities

Design, implement, and maintain highly available Elasticsearch clusters.
Develop efficient indexing strategies for structured and unstructured data.
Optimize search performance, relevance scoring, and query execution.
Monitor cluster health and troubleshoot performance bottlenecks.
Configure and maintain Elastic Stack components including: Elasticsearch, Kibana, Logstash, Beats, Elastic Agent
Implement Index Lifecycle Management (ILM), snapshots, backups, and disaster recovery strategies.
Design mappings, analyzers, tokenizers, and ingest pipelines.
Build dashboards and visualizations using Kibana.
Work with development teams to integrate Elasticsearch APIs into applications.
Perform cluster upgrades, scaling, and capacity planning.
Implement security using Elastic Security, RBAC, TLS, and authentication mechanisms.
Automate deployments using Infrastructure as Code (Terraform, Ansible, Helm, or similar).
Support production incidents and perform root cause analysis
Cluster Deployment: Installing, configuring, and scaling Elasticsearch nodes and indices.
Log & Metric Monitoring: Integrating database audit logs, alert logs, and OS metrics into the ELK Stack (Elasticsearch, Logstash, Kibana).
Search Optimization: Mapping definitions, query optimization, and shard/replica management for fast text-based analytics.
Troubleshooting: Monitoring cluster health, JVM heap tuning, and resolving node dropouts or lag issues