Sr. Elastic Engineer

Remote from: USA
Salary, yearly, USD: 170,000 - 205,000
Department: Software Engineering
Employment type: Full Time,
Job posted: 21 May 2026
Apply before: 21 Jun 2026
Experience level: Senior
Views / Applies: 49 / 10

About ECS

Partnering with leading organizations to deliver powerful solutions for a complex world.

Actively Hiring

AI Summary

ECS is seeking a Sr. Elastic Engineer to work remotely on a contingent basis. This role involves designing, deploying, and maintaining Elastic Stack environments at scale, including Elasticsearch, Kibana, Logstash, Beats, and Fleet. The engineer will build ingestion pipelines, automation scripts, and dashboards while integrating with cloud platforms and containerized environments. The position requires deep expertise in Elastic Stack, scripting, and observability, with a focus on performance optimization and reliability. Candidates must be eligible for U.S. security clearance and hold a passport.

Job Complexity

Easy Hard

AI Insight The role requires deep hands-on expertise with Elastic Stack, scripting, cloud platforms, and container orchestration, along with the ability to mentor and obtain security clearance, making it challenging but not the highest level.

Salary Analysis

AI Insight The offered salary range of $170,000 - $205,000 is competitive and above the market median for similar roles, reflecting the seniority and specialized skills required. The market range for Sr. Elastic Engineers in the US is typically $140,000 - $220,000, so this position is well-positioned.

Key Skills

Elasticsearch Kibana Logstash Python Kubernetes Terraform AWS Azure Observability Automation

Cover Letter Sample

Dear Hiring Manager,

I am writing to express my strong interest in the Sr. Elastic Engineer position at ECS. With over X years of hands-on experience designing and optimizing Elastic Stack environments, I am excited about the opportunity to contribute to your MSSP team. My expertise includes deploying Elasticsearch, Kibana, and Logstash across cloud and on-premises infrastructure, and I have a proven track record of building scalable ingestion pipelines and automation scripts using Python and Terraform.

I am particularly drawn to this role because of the emphasis on innovation and collaboration. In my previous role, I led the migration of a large-scale Elastic deployment to Kubernetes, improving system reliability by 30%. I am confident that my skills in observability, performance tuning, and mentoring align perfectly with your requirements.

Thank you for considering my application. I look forward to the possibility of discussing how I can drive value for ECS.

Sincerely,
Your Name

Possible Interview Questions

Can you describe your experience designing and deploying a production-scale Elastic Stack environment?

Yes, in my previous role I led the deployment of an Elastic cluster handling over 10TB of data daily. I designed the index strategy, set up sharding and replication, and used Logstash pipelines for data ingestion. I also implemented monitoring with Kibana dashboards and automated scaling using Terraform.

How do you approach optimizing Elasticsearch performance?

I start by analyzing query patterns and index mappings. I use techniques like index lifecycle management, proper shard sizing, and query optimization. I also monitor cluster health with Elastic's monitoring tools and adjust configurations like refresh intervals and thread pools based on workload.

Describe a time you integrated Elastic with other tools like SIEM or Splunk.

I integrated Elastic with Splunk by setting up a Logstash pipeline to forward data from Splunk to Elasticsearch. I also configured the Elastic SIEM app to correlate events from CrowdStrike and other sources, creating custom detection rules and dashboards for security teams.

How do you handle security and access control in Elastic Stack?

I implement role-based access control using Elasticsearch security features. I create roles with specific index privileges and use Kibana spaces to separate data. I also enable TLS for encryption and audit logging to track access.

What is your experience with containerizing Elastic Stack?

I have deployed Elasticsearch and Kibana on Kubernetes using Helm charts. I configured persistent volumes for data, used ConfigMaps for settings, and set up horizontal pod autoscaling based on CPU and memory. I also used Docker for local development and testing.

ECS is seeking a Sr. Elastic Engineer to work remotely. Please Note: This position is contingent upon additional funding.

Are you passionate about building and scaling Elastic environments and eager to make an immediate technical impact? Join ECS, a leading provider of cloud, AI, data, and enterprise transformation solutions. In this role, you will implement, optimize, and maintain Elastic Stack environments at scale while contributing to architecture, automation, and integrations that improve reliability, performance, and operational efficiency.

We are seeking a Sr. Elastic Engineer to join our Managed Security Services (MSSP) team. The ideal candidate has hands-on experience with Elastic Stack and enjoys designing, coding, and deploying complex systems. You will collaborate with engineering, DevOps, cloud, and client teams to deliver resilient, high-performance Elastic solutions while maintaining visibility into system health and operational workflows.

Key Responsibilities:

Design, deploy, and maintain Elastic Stack environments, including Elasticsearch, Kibana, Logstash, Beats, and Fleet.
Build and optimize ingestion pipelines, index strategies, search queries, dashboards, and automation workflows.
Write scripts, automation, and integrations (Python, PowerShell, Bash, etc.) to streamline operations, data processing, and monitoring.
Deploy Elastic across platforms, including on-premises, public cloud (AWS, Azure, GCP), GovCloud, and containerized environments (Kubernetes, Docker, OpenShift).
Leverage automation and orchestration tools such as Terraform, Ansible, CI/CD pipelines, and Docker to manage deployments and operational tasks.
Integrate Elastic with enterprise tools such as SIEMs, Splunk, CrowdStrike, and other telemetry sources.
Monitor system health, troubleshoot performance issues, and optimize for reliability and scalability.
Lead design reviews, provide guidance on Elastic best practices, and support knowledge sharing across teams.
Maintain documentation for architectures, deployment patterns, runbooks, and operational best practices.
Stay current with Elastic features, certifications, and emerging platform capabilities.

Salary Range: $170,000 – $205,000

General Description of Benefits

Qualifications

Deep, hands-on expertise with Elastic Stack (Elasticsearch, Kibana, Logstash, Beats, Fleet).
Strong scripting and automation skills (Python, PowerShell, Bash, etc.).
Solid understanding of observability, logging, metrics, and distributed systems.
Experience designing, deploying, and optimizing production-scale Elastic environments.
Strong Linux/Unix, networking, and cloud platform knowledge (AWS, Azure, GCP).
Ability to mentor, guide, and influence engineering teams on Elastic best practices.
Outstanding verbal and written communication skills.
Willingness and ability to support domestic or international on-site engagements.
U.S. Passport required.
Must be eligible to obtain a U.S. Security Clearance.

Apply now >