Senior Elastic Stack Data Integration Engineer IRES - SSFB/HSV

Amentum•Colorado Springs, CO

7d•Onsite

About The Position

The Senior Elastic Stack Data Integration Engineer will serve as the primary technical authority for designing, building, and maintaining data ingestion pipelines supporting Elastic SIEM. This role focuses on creating scalable, resilient Logstash architectures; developing advanced pipeline logic; normalizing, enriching, and transforming security telemetry; and ensuring reliable delivery of high-fidelity data to Elasticsearch. The successful candidate will have deep command of Logstash architecture, patterns, and performance optimization, mastery of parsing, enrichment, normalization, and ECS alignment, and strong understanding of network protocols, logging patterns, and telemetry generation from enterprise systems. Advanced troubleshooting skills across data ingestion, pipeline logic, and Elastic Stack processing layers are essential. The candidate must be capable of designing scalable, HA ingestion workflows with clear operational boundaries, and able to conduct data modeling, schema design, and transformation mapping. They should be effective at interfacing with multiple teams, gathering requirements, and aligning pipeline designs with SIEM analytics needs, with a focus on reliability, maintainability, and observability across all pipeline components. Strong attention to detail and a disciplined approach to documentation, versioning, and configuration management are required. The candidate must demonstrate the ability to work independently, drive pipeline architecture decisions, and mentor junior engineers. Strong documentation, workflow diagramming, and communication skills are also necessary.

Requirements

Deep command of Logstash architecture, patterns, and performance optimization.
Mastery of parsing, enrichment, normalization, and ECS alignment.
Strong understanding of network protocols, logging patterns, and telemetry generation from enterprise systems.
Advanced troubleshooting skills across data ingestion, pipeline logic, and Elastic Stack processing layers.
Capability to design scalable, HA ingestion workflows with clear operational boundaries.
Ability to conduct data modeling, schema design, and transformation mapping.
Effective at interfacing with multiple teams, gathering requirements, and aligning pipeline designs with SIEM analytics needs.
Focused on reliability, maintainability, and observability across all pipeline components.
Strong attention to detail and a disciplined approach to documentation, versioning, and configuration management.
Demonstrated ability to work independently, drive pipeline architecture decisions, and mentor junior engineers.
Strong documentation, workflow diagramming, and communication skills.
4 or more years of general (full-time) work experience (may be reduced with completion of advanced education).
3 or more years of directly related experience in log ingestion, data engineering, or SIEM pipeline development.
2 or more years of experience working in a management or leadership role.
Strong background in Elastic Stack components (Elasticsearch, Kibana, Beats, Elastic Agent).
Experience with data ingestion, processing, and enrichment techniques.
Hands-on experience ingesting, processing, and normalizing diverse log types (Windows events, syslog, firewall logs, cloud telemetry, security tooling).
Proficient with Linux administration, system-level debugging, and CLI-based operations.
DoD 8570.01-M IAT Level II certification with Continuing Education (CE) - (CCNA-Security, CySA+, GICSP, GSEC, Security+ CE, CND, SSCP).
Active DoW Secret Security Clearance.
Must be able to obtain, and maintain, a DoW Top Secret Security Clearance.

Nice To Haves

Elastic Certified Engineer or relevant Elastic Stack certifications.
Strong experience integrating Kafka, Redis, or other message bus technologies into ingestion workflows.
Scripting proficiency in Python, Bash, or PowerShell for automation and data validation.
Experience designing geo-distributed or multi-cluster ingestion architectures.
Knowledge of threat intelligence ingestion, correlation data enrichment, and advanced ECS mapping.
Experience with CI/CD pipelines, GitOps workflows, or Infrastructure-as-Code (Terraform, Ansible).
Familiarity with data quality assurance frameworks and pipeline testing methodologies.
Knowledge of cloud-native logging architectures (AWS Firehose, Azure Event Hub, GCP Logging).

Responsibilities

Architect, build, and maintain Logstash pipelines to ingest and transform logs from diverse systems, including network devices, servers, cloud services, and security platforms.
Implement parsing, grok patterns, JSON transformations, conditional routing, enrichment logic, and ECS mapping.
Optimize pipeline performance, resiliency, and scalability (e.g., persistent queues, pipeline workers, memory tuning, load balancing).
Ensure all ingested data aligns to ECS (Elastic Common Schema) or internal schema requirements.
Implement data enrichment workflows (GeoIP, threat intel lookups, metadata injection).
Validate data completeness, integrity, and fidelity across ingestion flows.
Maintain and optimize Logstash clusters, including version management, scaling, tuning, and high-availability configurations.
Manage integrations with Beats, Elastic Agent, Kafka, syslog endpoints, and custom data collectors.
Monitor ingestion throughput, latency, and error rates; implement proactive alerting and troubleshooting processes.
Create and maintain technical documentation, including pipeline diagrams, data flow maps, runbooks, and schema references.
Establish enterprise standards for parsing, enrichment, normalization, and ingestion patterns.
Support internal and external audits by documenting data handling flows and pipeline logic.
Work closely with SIEM integration engineers to align pipelines with customer environments and logging requirements.
Partner with detection engineering teams to ensure data supports analytic coverage and rule development.
Collaborate with infrastructure and platform operations for deployment, scaling, and reliability engineering.