Senior Information Security Infrastructure Engineer, Elastic

• Ability to operate Elastic and Elasticsearch in production. This includes managing ingest pipelines, index templates, mappings, and queries, while ensuring that clusters are healthy and upgraded. Experience with ECK or Elasticsearch on Kubernetes is strongly preferred
• Proven track record of using AI to accelerate development, debug complex systems, and accelerate operations, while still owning the final outcomes
• Kubernetes – deploying and operating workloads (scheduled jobs, Helm charts, operators); troubleshooting pods/jobs in a cluster
• Terraform – managing cloud and Elasticsearch resources as code
• API integration – consuming REST APIs for data ingestion: authentication, pagination, rate limiting concurrency, and error handling
• Python scripting – able to read, write, and modify ingestion/automation scripts. Scripting-level, not full software-engineering depth
• Eligibility to work in Department of Defense (DoD) Impact Level 4 or above cloud service environments

Desirable

• Join the InfoSec - Security Architecture team as a Senior Information Security Engineer, where you'll have the chance to play a key role in protecting our organization's data and systems
• This position offers the opportunity to work on meaningful projects that directly impact our security posture and collaborate with a skilled team passionate about information security
• In more detail: We ingest the security telemetry that the whole security org runs on
• In this role you'll get that telemetry into Elasticsearch consistently and keep the underlying Elastic clusters healthy
• You own the pipeline end to end: from a new security data source landing, through ingest pipelines, into the indices detection, IR, and consulting teams query, plus the clusters that store it all
• Security telemetry ingestion – build and maintain ingestion of security-relevant data into Elasticsearch (cloud provider audit logs, identity/SaaS activity, endpoint and asset data). This means integrating with third-party and cloud provider APIs to pull telemetry: auth, pagination, rate limits, and handling schema changes. Both Elastic integrations and one-off custom integrations
• Keep the Elastic Cloud on Kubernetes clusters healthy. Monitor and upgrade them regularly. This involves updating versions and builds. Manage capacity and shards. Handle index lifecycle management (ILM). Enable cross-cluster search (CCS)
• Use Terraform to manage cloud infrastructure and Elasticsearch resources. This includes managing pipelines, index templates, and alerts. Utilize Kubernetes and Helm to deploy scheduled ingest jobs
• Use AI automation and tooling to reduce toil. This includes self-healing jobs and health checks. It also involves alerting, internal CLIs, and AI or agent-assisted workflows for investigation and operations
• Data quality & reliability – Own the "is the security data actually flowing correctly?" question. Monitor backfills. Ensure that schemas and fields are consistent. Keep an eye on costs