Data Architect (DataOps)

Location: Chantilly, VA
Security Clearance: Active TS or higher [Required] Must be able to obtain CI Polygraph
Job Type: Full-Time
Target Salary Range*: $160,000 - $195,000

*This represents the potential salary range for this position depending on education level, years of experience and/or certifications in addition to other position specific requirements which may impact salary

Lead the design and management of the enterprise data environment to ensure information is accurate, connected, and ready to support advanced analytics and AI. Integrate traditional databases, modern data platforms, and semantic and knowledge-graph technologies to support data discovery and decision-making.

Establish data standards, ensure secure data sharing across environments, and collaborate with engineering and AI teams to maintain structured, governed, high-quality data.

• Design and maintain enterprise data architectures that integrate multiple data systems.
• Evaluate new technologies and recommend improvements to data platforms and processes.

• Build and manage knowledge graphs, ontologies, and metadata structures to improve data context and discovery.
• Provide structured, contextual data to AI and analytics teams for model development and retrieval workflows.

• Develop secure, well-governed data pipelines and processes that deliver high-quality, trusted data.
• Establish organization-wide data governance, data quality, and metadata standards.

• Ensure data sharing and access comply with security, compliance, and zero-trust requirements.

• Bachelor’s degree. [Required]

• Minimum of 10 years of experience in data architecture, data engineering, or related areas. [Required]
• Strong experience with enterprise data platforms, distributed systems, and modern data pipelines. [Required]
• Experience with metadata management, data lineage, and data governance processes. [Required]

• Familiarity with semantic technologies, including RDF, OWL, and SPARQL; ontologies; and knowledge graph design. [Required]
• Knowledge of cloud data services and secure data integrations across multiple environments. [Required]
• Ability to communicate complex technical concepts clearly to non-technical stakeholders. [Required]

• In-house custom-developed application.

• Multi-model and multi-modality orchestration involving reasoning, perception, memory, and tool execution.
• Temporal, spatial, geographic, quantitative, and computer vision AI implementation.
• Named Entity Recognition (NER), contextual entity extraction, and resolution.
• Multi-step retrieval and structured output generation.
• Complex data parsing and streamlined ingestion.
• Deep source citation and traceable analytic outputs.

• Vector, graph, and multi-database orchestration.
• Automated ETL pipeline creation, scheduling, and optimization.
• Attribute-Based Access Control (ABAC) at the data layer.
• Provenance and end-to-end data lineage tracking.
• Schema-aligned ingestion and normalization of diverse structured and unstructured data.

• Air-gapped, hybrid, and on-premises operational environments.
• Container orchestration and distributed systems management using Kubernetes.
• Locally hosted GPU servers and hyperconverged nodes.
• Multitenant Cross-Domain Solutions (CDS).

• UI, backend, and middleware engineering using TypeScript, React, Python, FastAPI, LanceDB, lance graph, PostgreSQL, Temporal.io, Docker, and Kubernetes.
• Integration of reasoning components, retrieval systems, databases, and AI / GenAI services.
• Use of modern programming languages and frameworks.

• 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.