Lead Platform Engineer

Keller Schroeder • Contract • Baltimore, MD, US • 2w ago

NO PHONE CALLS PLEASE! Calls will not be returned.

We welcome individual candidates and Corp-to-Corp (CTC) resume submissions.

6-month contract

Hybrid: Must live within 50 miles of Baltimore MD, Evansville IN, Charlotte NC, Fort Worth TX, Irving TX, or Wilmington DE.

NO PHONE CALLS PLEASE! Calls will not be returned.

We are seeking a Sr. Platform Engineer to help our direct client. This is a hybrid position. Must live within 50 miles of Baltimore, MD, Wilmington, DE, Charlotte, NC, Dallas, TX, New York, NY, or Evansville, IN. Occasional travel to work out technical approach, dependencies, and delivery commitments.

This individual will own the end-to-end lifecycle of the enterprise-grade Elasticsearch platform. This role combines deep technical expertise in Elasticsearch with platform engineering rigor to design, deploy, operate, and optimize large-scale search, observability, and log analytics solutions. You will serve as the subject-matter expert (SME) for Elasticsearch ensuring high availability, performance, security, and compliance across cloud and on-prem deployments. Collaborating with architecture, development, security, and operations teams, you will drive scalability, automate workflows, and enable data-driven applications while maintaining operational excellence in an Agile delivery model.

Responsibilities:

Platform Design & Development

• Architect and deploy scalable Elasticsearch solutions for search, observability, and logs/metrics analytics use cases.

• Design and implement ELK/Elastic Stack (Elasticsearch, Logstash, Kibana) and complementary pipelines.

• Create and manage multi-node clusters across availability zones in cloud and/or on-prem environments.

• Translate business requirements into technical designs, including indexing strategies, shard allocation, and data modeling

Operations & Support

• Monitor, maintain, and troubleshoot ELK/Elastic environments and Elasticsearch clusters for performance, stability, and data integrity.

• Perform performance tuning namely query optimization, indexing pipelines and shard rebalancing.

• Conduct capacity planning, configuration management, and continuous improvement via metrics, alerts, and automation.

• Execute version upgrades, patching, and backward-compatible migrations with minimal downtime.

Automation & DevOps Integration

• Build and maintain Infrastructure as Code (Terraform/Ansible), CI/CD pipelines, and automation scripts (Python, Bash, etc.)

• Integrate Elasticsearch with cloud providers, cloud-native services, and other open-source observability tools.

• Enable self-service capabilities for development teams via APIs, templates, and dashboards

Collaboration & Enablement

• Partner with DevOps, Security, and AppDev teams to integrate Elasticsearch into microservices, CI/CD, and monitoring workflows.

• Provide expert guidance, code/config reviews, and lightweight scripting to accelerate feature delivery.

• Create and maintain runbooks, architecture diagrams, KPIs dashboards (Kibana), and troubleshooting guides.

Innovation & Continuous Improvement

• Evaluate and adopt new Elastic features, plugins, and ecosystem tools.

• Lead proof-of-concepts for advanced use cases (machine learning, cross-cluster replication etc.)

• Identify automation opportunities and drive platform resilience initiatives.

Requirements:

Key Skill: Elasticsearch administration

• 3 – 5 years hands-on Elasticsearch administration experience operating Elasticsearch clusters in enterprise environments, including configuration, maintenance, upgrades, patching, and monitoring. Deep expertise in Elasticsearch architecture

• Strong understanding of nodes, shards, replicas, indexing strategies, query optimization, aggregations, APIs, and cluster scaling.

• Proven experience deploying and managing the Elastic Stack (ELK) ability to design, deploy, and administer Logstash, Kibana, APM, Beats, Fleet Server, ILM policies, and ingestion pipelines.

• Scripting and automation proficiency skilled with Python, Bash, Terraform, Ansible, and similar tools to automate cluster operations, monitoring, and CI/CD workflows.

• Understanding of distributed systems, networking and storage knowledge of core distributed systems principles, load balancers (including Nginx), networking, and storage backends incident management, troubleshooting, and root cause analysis.

• Ability to diagnose ingestion issues, log disruptions, cluster health problems, and performance bottlenecks across the Elastic ecosystem.

We look forward to receiving your resume in PDF format!

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