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Dark Wolf Solutions

Data Engineer - Expert

Chantilly/Herndon, VA$165k–$200kfull-timemidAdded today

About this role

Dark Wolf seeks an Expert Data Engineer to architect and lead enterprise-grade data platforms for defense and intelligence missions. You'll design petabyte-scale distributed systems, optimize processing frameworks, implement security controls, and mentor engineering teams in multi-cloud and air-gapped environments.

What you'll do

  • Lead architectural design and implementation of distributed batch and streaming data platforms
  • Optimize Spark, Trino/Presto, and other execution engines for petabyte-scale datasets under strict SLAs
  • Provision data platform infrastructure using Infrastructure as Code across multi-cloud environments
  • Establish zero-trust security, data lineage tracking, and automated compliance scanning for regulated data
  • Act as Technical Pod Lead driving Agile deliverables, technical standards, and mentoring

What they're looking for

  • Apache Spark, Flink, Kafka, Ray, and workflow orchestration
  • Distributed systems internals, LSM trees, lakehouse formats (Delta Lake, Iceberg, Hudi)
  • Python, Scala, SQL, and Go programming
  • Kubernetes cluster management and custom operators
  • Terraform and multi-cloud infrastructure architecture
  • DevSecOps pipelines and automated compliance testing
  • Enterprise data modeling and streaming schema contracts
  • Technical leadership and Agile methodologies
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Dark Wolf Solutions

Dark Wolf Solutions builds DevSecOps platforms, integration systems, and data analytics solutions for defense and intelligence customers, with a focus on DoD and Space Force operations. The company is hiring DevOps engineers, full-stack software engineers, systems engineers, and data engineers to support cloud infrastructure, platform development, radar systems, and mission-critical defense applications.

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Likely interview questions

  • Describe your experience optimizing distributed query engines like Spark or Presto for petabyte-scale workloads—what specific bottlenecks did you address?
  • How have you designed and implemented data mesh architectures, and what governance patterns did you establish?
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