Data Engineer

Peraton•Herndon, VA

91d•$104,000 - $166,000•Onsite

About The Position

We are seeking a highly skilled and innovative Data Engineer to join our team in the greater DMV area, supporting the Army National Guard. This role involves leading the design, build, and optimization of data pipelines to ingest, normalize, and store logs, events, and telemetry for various security and analytics use cases. The engineer will implement schemas, mappings, ETL/ELT logic, and data models to support correlation, threat hunting, reporting, dashboards, and evidence generation for RMF/CCRI. Responsibilities include onboarding new data sources, ensuring data quality and pipeline reliability, and collaborating with stakeholders to meet analytic requirements. The position also requires developing and maintaining data flow documentation, implementing data retention strategies, building automated testing and observability for pipelines, and supporting production troubleshooting and incident response. Mentoring junior engineers and driving continuous improvement are also key aspects of this role.

Requirements

Minimum of 8 years with BS/BA; Minimum of 6 years with MS/MA; Minimum of 3 years with PhD
Active TS/SCI clearance.
Master’s degree in Computer Science, Engineering, Information Technology, Data Science, or a related field; OR Relevant DoD/military training (example: SANS SEC545 (Cloud Security Architecture)); OR Relevant professional certification or equivalent experience (examples: AWS Certified Solutions Architect – Professional; Microsoft Certified: Power Platform Solution Architect Expert; Microsoft Certified: Azure Solutions Architect Expert; CISSP‑ISSAP).
Data engineering, analytics pipeline, or platform engineering experience with at least 3 years building large‑scale telemetry/log pipelines for security or operations use cases.
Strong expertise in ETL/ELT design, schema design, data modeling, stream/batch processing (Kafka, Spark, Flink, etc.), and data storage technologies (Elasticsearch, Splunk, data lake, cloud data warehouses).
Proficiency in programming/scripting (Python, Java, Scala), infrastructure as code, and CI/CD for data pipelines.
Experience with data quality frameworks, observability (metrics/logs/tracing), and automated validation/testing of ingestion pipelines.
Familiarity with security telemetry (IDS/IPS, EDR, network flows), threat intelligence feeds, CDAP outputs, and requirements for RMF/CCRI evidence and auditability.
Ability to produce data lineage, codebooks, and documented data flows for auditors and analysts.

Nice To Haves

Prior DoD/ARNG/enterprise experience integrating classified and unclassified telemetry sources.
Experience optimizing storage/cost for high‑volume telemetry and implementing secure cross‑domain data handling patterns.
Familiarity with SOC analytic platforms, detection engineering workflows, and delivery of data for threat hunting and CDAP analyses.

Responsibilities

Lead design, build, and optimization of data pipelines to ingest, normalize, and store logs, events, and telemetry for SOC, DCO, vulnerability management, CDAP, and analytics use cases.
Implement schemas, mappings, ETL/ELT logic, and data models to support correlation, threat hunting, reporting, dashboards, and RMF/CCRI evidence generation.
Onboard and integrate new data sources (OT/DCI telemetry, threat feeds, CDAP outputs, cloud logs), ensuring proper parsing, enrichment, and normalization.
Monitor data quality, latency, completeness, and pipeline reliability; identify gaps and implement remediation or fallback strategies.
Collaborate with detection engineers, analysts, and data consumers to translate analytic requirements into pipeline designs and delivery SLAs.
Develop and maintain data flow documentation, schemas, codebooks, and lineage artifacts to support traceability and auditability.
Implement data retention, partitioning, and storage optimization patterns to balance performance, cost, and forensic requirements.
Build automated validation, testing, and observability for pipelines (data health checks, anomaly detection, alerting).
Support production troubleshooting, incident response data needs, and timely delivery of evidentiary exports for assessments.
Mentor junior engineers and drive continuous improvement of data engineering practices and tooling.