Senior Data Engineer (FedD238)

Defense Unicorns is hiring a Senior Data Engineer to embed with a government team and its technology partners to build an AI-powered engineering ecosystem. The role establishes secure, reliable data foundations and repeatable patterns so AI models, agents, and mission applications can access trusted, discoverable data.

Responsibilities:

• Design, build, and maintain reliable data pipelines that ingest, transform, validate, and deliver data for AI models, agents, and mission applications
• Integrate structured and unstructured data sources into enterprise data services, including relational, document, vector, and graph-oriented technologies
• Work closely with AI/agent engineers to make data discoverable and usable for RAG, semantic search, model context, and tool-enabled agent workflows
• Develop repeatable data ingestion and transformation patterns using Python, SQL, APIs, event-driven architectures, and workflow/orchestration technologies
• Establish data quality, validation, provenance, lineage, and metadata practices so downstream users and AI systems can trust the information they consume
• Design data access patterns that enforce appropriate identity, authorization, classification, and security boundaries while minimizing unnecessary friction for developers and mission users
• Collaborate with platform engineers to operationalize data services within Kubernetes/OpenShift environments and integrate them into CI/CD and GitOps workflows
• Help integrate enterprise data capabilities such as relational stores, JSON/document data, vector search, graph data, advanced analytics, and RAG services into the broader platform architecture
• Troubleshoot end-to-end data issues spanning source systems, pipelines, storage, APIs, identity, networking, platform services, and AI applications
• Work directly with government engineers and commercial technology partners to resolve data dependencies and remove blockers to mission delivery
• Develop documentation, data contracts, schemas, architecture decision records, runbooks, and operational standards that make successful patterns repeatable across environments
• Identify recurring data engineering challenges that can be standardized, automated, or productized rather than repeatedly solved through manual engineering
• Operate effectively in a fast-moving, ambiguous environment where data sources, architectures, security requirements, and mission priorities will continue to evolve

Requirements:

• Active TS/SCI clearance
• 7+ years of experience in data engineering, data platform engineering, or a 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 other distributed integration patterns
• Working knowledge of data modeling, schema design, data quality, lineage, metadata, and data governance concepts
• Experience operating in cloud-native or containerized environments and collaborating with Kubernetes/platform engineering 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 working data solutions
• GEOINT experience
• Experience supporting AI/ML workloads, including RAG, embeddings, vector search, model context, or agentic applications
• Experience with vector databases, graph databases, document databases, knowledge graphs, or semantic search technologies
• Experience with Oracle or comparable enterprise data platforms supporting relational, JSON/document, vector, graph, and analytics workloads
• Experience designing data products or platforms for high-scale, low-latency, or mission-critical workloads
• Experience with data orchestration and workflow tools such as Airflow, Dagster, Argo Workflows, or comparable technologies
• Experience with streaming/event platforms such as Kafka, Redpanda, NATS, or similar technologies
• Experience deploying data services using Kubernetes, Helm, GitOps, Infrastructure as Code, and automated CI/CD pipelines
• Experience supporting data and AI systems in Secret or TS/SCI environments and across multiple security domains
• Experience with disconnected or air-gapped environments and the challenges of moving data, metadata, and software across security boundaries
• Familiarity with NIST 800-53, RMF/ATO processes, zero-trust architectures, and data security/compliance practices
• Experience with enterprise search, RAG pipelines, data catalogs, data contracts, or knowledge-graph architectures
• Familiarity with UDS, Zarf, Pepr, Iron Bank, or similar secure software delivery technologies
• Experience working on multidisciplinary teams consisting of government engineers, FDEs, OEM professional services teams, and multiple technology vendors
• Demonstrated ability to take a fragmented or difficult data environment and turn it into a reliable, reusable foundation for mission applications and AI