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Defense Unicorns is seeking a Senior Data Engineer to embed with a government team and technology partners building an AI-powered engineering ecosystem. This role focuses on making enterprise data usable, discoverable, governed, and accessible to AI models, agents, and mission applications.
You will work across the full data lifecycle: data ingestion, transformation, storage, metadata management, APIs, search, vector and graph technologies, and interfaces between the data layer and AI workloads. This is not a traditional analytics or BI role—the focus is on building data foundations and repeatable patterns that allow AI and agentic capabilities to access trusted data and move from prototype to production in secure and classified environments.
Key responsibilities include:
- Design, build, and maintain reliable data pipelines for AI models, agents, and mission applications
- Integrate structured and unstructured data sources into enterprise data services (relational, document, vector, graph)
- Collaborate with AI/agent engineers to make data discoverable for RAG, semantic search, and agent workflows
- Develop repeatable data ingestion and transformation patterns using Python, SQL, APIs, and orchestration technologies
- Establish data quality, validation, provenance, lineage, and metadata practices
- Design data access patterns enforcing identity, authorization, classification, and security boundaries
- Operationalize data services within Kubernetes/OpenShift environments and CI/CD workflows
- Integrate enterprise data capabilities (relational, JSON, vector search, graph, analytics, RAG) into platform architecture
- Troubleshoot end-to-end data issues spanning source systems, pipelines, storage, APIs, identity, networking, and applications
- Work directly with government engineers and commercial technology partners to resolve dependencies
- Develop documentation, data contracts, schemas, architecture decision records, and operational standards
- Identify recurring challenges that can be standardized, automated, or productized
- Operate effectively in a fast-moving, ambiguous environment where architectures and priorities evolve
The ideal candidate is a hands-on data engineer comfortable entering evolving architectures, learning unfamiliar systems quickly, working with commercial partners, and solving difficult problems involving data quality, lineage, access, performance, and security.
REQUIREMENTS:
- Active TS/SCI clearance (required)
- 7+ years of experience in data engineering, data platform engineering, or closely related field
- Strong proficiency with SQL and Python for data processing, automation, and integration
- Hands-on experience building and operating production data pipelines and ETL/ELT workflows
- Experience with relational databases and modern data storage patterns for structured and unstructured data
- Experience integrating data through REST APIs, message/event systems, or distributed integration patterns
- Working knowledge of data modeling, schema design, data quality, lineage, metadata, and data governance
- Experience operating in cloud-native or containerized environments and collaborating with Kubernetes/platform teams
- Ability to troubleshoot data issues across infrastructure, applications, APIs, storage, networking, and access controls
- Ability to work independently, rapidly learn new technologies, and translate ambiguous mission needs into solutions
- Local to National Capital Region and able to support onsite work at government facility in Springfield, VA
PREFERRED EXPERIENCE:
- Supporting AI/ML workloads (RAG, embeddings, vector search, model context, agentic applications)
- Vector databases, graph databases, document databases, knowledge graphs, semantic search
- Oracle or comparable enterprise data platforms supporting relational, JSON, vector, graph, and analytics
- Designing data products or platforms for high-scale, low-latency, or mission-critical workloads
- Data orchestration tools (Airflow, Dagster, Argo Workflows)
- Streaming/event platforms (Kafka, Redpanda, NATS)
- Deploying data services using Kubernetes, Helm, GitOps, Infrastructure as Code, CI/CD
- Supporting data and AI systems in Secret or TS/SCI environments and across security domains
- Disconnected or air-gapped environments and challenges of moving data across security boundaries
- NIST 800-53, RMF/ATO processes, zero-trust architectures, data security/compliance
- Enterprise search, RAG pipelines, data catalogs, data contracts, knowledge-graph architectures
- UDS, Zarf, Pepr, Iron Bank, or similar secure software delivery technologies
- Multidisciplinary teams with government engineers, FDEs, OEM professional services, multiple vendors
- Demonstrated ability to transform fragmented data environments into reliable, reusable foundations
