Data Engineer
Defense UnicornsUnited States - RemotePosted 26 March 2026
Job Description
EMPLOYER IS A CONTRACTOR FOR THE U.S. GOVERNMENT. THIS POSITION WILL REQUIRE U.S. CITIZENSHIP.
Responsibilities:
Designing data workflows, pipelines, and pathways to support ETL/ELT, while ensuring data-quality and exchange of sensitive information across AWS cloud and on-prem environments.
Implementing systems engineering principles to ensure the reliability, scalability, and maintainability of data layer systems, including backup and recovery strategies
Architecting, deploying, and operating data streaming technologies (Kafka, Redpanda, Kinesis, etc ) on Kubernetes with an emphasis on declarative definitions to reduce complexity in day-2 operations
Working with a platform team to implement best practices for configuration management, security, and performance optimization within Kubernetes and AWS environments
Working with a platform team to automate infrastructure provisioning, monitoring, and scaling using tools such as Terraform working with cybersecurity SMEs to incorporate best practices and techniques to defend our data layer against potential threats
Ensuring that our data layer systems are compliant with industry and DoD regulations, standards, and best-practices
Staying up-to-date with industry trends and emerging technologies related to Kubernetes, containerization, and data engineering
Travel Expectations/Requirements: 6-10 times per year
The listed responsibilities are not exhaustive and additional responsibilities may be assigned based on the evolving needs of the organization. We are seeking a dynamic individual who is able to adapt and take on new responsibilities as they arise.
* Preferred Experience and Qualifications:
Demonstrated experience (minimum 5 years) in building and maintaining production data pipelines, databases, and data exchange services on Kubernetes Experience in designing, deploying, and operating Kafka / Redpanda data streaming solutions on Kubernetes
Experience in creating data pipelines from scratch with the ability to process data in large volumes and manage data stores
Experience with containerization technologies such as Docker and container orchestration systems like Kubernetes
Knowledge of systems engineering principles to ensure the reliability, scalability, and maintainability of data layer systems Familiarity with API Security, Container Security, AWS Cloud Security Familiarity with cybersecurity principles and best practices for securing data layer systems
Excellent problem-solving skills, with the ability to work independently and as part of a team
Excellent communication skills, with the ability to convey complex ideas to both technical and non-technical team members
The selected candidate will be subject to a pre-employment background check ("CBI") and must be able to obtain and maintain a Secret-level DoD security clearance
Experience with machine learning and generative AI models
Full compensation packages are based on candidate experience. Compensation ranges are established using national benchmarking data and apply across all geographic locations within the United States.
Remote - USA
$124,250
$166,750 USD
Who We Are
Defense Unicorns delivers mission value by streamlining software delivery so our customers can focus on the most important challenges. We share a vision of freedom and security for the advancement of progress and innovation. Our commitment to this vision, and to our mission-driven customers, means a commitment to speed, user experience and optionality, without compromising security. Our team is composed of innovators, software engineers, and veterans with decades of experience delivering technology programs across the federal market.
What We Do
We create and deliver secure solutions for continuous software integration and delivery. Defense Unicorns consolidates the best practices for security pipelines, testing, and deployment automation in order to meet the high security requirements valued by mission owners. Our solutions are agn ... (truncated, view full listing at source)
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