Data Architect SME (Top Secret clearance required)

i3•Chantilly, VA

23h•Onsite

About The Position

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. The role brings together traditional databases, modern data platforms, and semantic and knowledge-graph technologies to help teams access meaningful insights and support decision-making. The SME sets data standards, secures data sharing across environments, and collaborates with engineering and AI teams to keep data structured, governed, and high quality. The role includes designing enterprise data architectures, building knowledge graphs and ontologies, developing governed data pipelines, and supporting AI and analytics teams with structured, contextual data. It also evaluates technologies and ensures data access and sharing comply with security, compliance, and zero trust requirements. The position is onsite in Chantilly, Virginia, and requires a Top Secret clearance, a bachelor’s degree, and at least 10 years of relevant experience.

Requirements

At least 10 years of experience in 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 nontechnical stakeholders.
Bachelor’s degree required.
Top Secret clearance required.
Technical experience involving multi-model and multimodality orchestration; temporal, spatial, geographic, quantitative, or computer vision AI; named entity recognition and entity resolution; multi-step retrieval; structured output generation; complex data parsing; and source citation and traceable analytic outputs.
Experience or technical capability involving Kubernetes, distributed systems, locally hosted GPU servers, hyperconverged nodes, and multitenant CrossDomain Solutions.
Application development and integration experience involving TypeScript, React, Python, FastAPI, LanceDB, lance graph, PostgreSQL, Temporal.io, Docker, and Kubernetes.

Nice To Haves

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 a related field.

Responsibilities

Design and maintain enterprise data architectures that integrate multiple data systems.
Build and manage knowledge graphs, ontologies, and metadata structures to improve data context and discovery.
Develop secure, well-governed data pipelines and processes that produce high-quality, trusted data.
Provide structured, contextual data to support AI and analytics 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.
Create, schedule, and optimize automated ETL pipelines.
Orchestrate vector, graph, and multiple database systems; support schema-aligned ingestion and normalization of structured and unstructured data.
Implement attribute-based access control at the data layer and provenance and end-to-end data lineage tracking.
Support operations in air-gapped, hybrid, and on-premise environments, including multitenant CrossDomain Solutions.
Integrate reasoning components, retrieval systems, databases, and AI/GenAI services.
Develop and integrate application UI, backend, and middleware components.