Data Architect SME (Top Secret clearance required)

Data Architect SME Top Secret Required Location: Chantilly, VA on site Bachelors required Minimum 10 Years Experience The Data Architect (SME) leads the design and management of the organization’s enterprise data environment, ensuring information is accurate, connected, and ready to support advanced analytics and AI. This role brings together traditional databases, modern data platforms, and semantic/knowledge-graph technologies to help teams access meaningful insights and power smarter decision-making. The SME also sets data standards, ensures secure data sharing across environments, and collaborates closely with engineering and AI teams to keep data structured, governed, and high quality. Key Responsibilities Design and maintain enterprise data architectures that integrate multiple data systems. Build and manage knowledge graphs, ontologies, and metadata structures that improve data context and discovery. Develop secure, well governed data pipelines and processes for high quality, trusted data. Support AI and analytics teams by providing structured, contextual data for model development and retrieval workflows. Establish data governance, data quality, and metadata standards across the organization. Evaluate new technologies and recommend improvements to data platforms and processes. Ensure data sharing and access follow security, compliance, and zero trust requirements. Required Qualifications 10+ years of experience Data architecture, data engineering, or related areas. Strong experience with enterprise data platforms, distributed systems, and modern data pipelines. Familiarity with semantic technologies (RDF, OWL, SPARQL), ontologies, and knowledge graph design. Experience with metadata management, data lineage, and data governance processes. Knowledge of cloud data services and secure, multi environment data integrations. Ability to communicate complex technical concepts clearly to non technical stakeholders. Unique Skills / Technical Requirements In-house custom developed application Advanced AI, GenAI & Multi-Model EngineeringMulti-model and multi-modality orchestration (reasoning, perception, memory, tool execution) Temporal, spatial, geographic, quantitative, and computer vision AI implementationNamed Entity Recognition (NER), contextual entity extraction, and resolutionMulti-step retrieval and structured output generationComplex data parsing and streamlined ingestionDeep source citation and traceable analytic outputs Data Architecture & ETLVector, graph, and multi database orchestrationAutomated ETL pipeline creation, scheduling, and optimizationAttribute Based Access Control (ABAC) at the data layerProvenance and end to end data lineage trackingSchema aligned ingestion and normalization of diverse structured and unstructured data Enterprise Infrastructure & CrossDomain SolutionsAir gapped, hybrid, and on premise operational environmentsContainer orchestration and distributed systems management (Kubernetes)Locally hosted GPU servers and hyperconverged nodesMultitenant CrossDomain Solutions (CDS) Application Development & IntegrationUI, backend, and middleware engineering (TypeScript, React, Python, FastAPI, LanceDB, lance graph, PostgreSQL, Temporal.io, Docker, Kubernetes) Integration of reasoning components, retrieval systems, databases, and AI / GenAI services Use of modern programming languages and frameworks Preferred Qualifications Experience supporting AI/ML teams, retrieval architectures, or vector-based search. Background working in secure, multi enclave, or zero trust environments. Experience with streaming data, real time processing, or hybrid cloud architectures. Advanced degree in Computer Science, Information Science, Data Engineering, or related field.