Senior Data Engineer

• 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.
• Local to the National Capital Region and able to support onsite work at a government facility in Springfield, VA as mission needs increase.
• 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.

Mission Focus

Make trusted data available where AI needs it, while establishing the repeatable, secure data patterns required to move from experimentation to operational capability.