Data Architect (DataOps) | SME

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 - $189,000

*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

The Data Architect (DataOps) | SME leads the design, integration, governance, and optimization of the organization’s enterprise data environment, ensuring information is accurate, connected, secure, and prepared to support advanced analytics and AI capabilities.

This role integrates traditional databases, modern data platforms, and semantic and knowledge-graph technologies to improve data discovery, contextual understanding, and decision-making. The Data Architect establishes enterprise data standards, enables secure data sharing across complex environments, and collaborates closely with engineering, analytics, and AI teams to maintain structured, governed, traceable, and high-quality data.

• Design and maintain enterprise data architectures integrating multiple data systems and platforms.
• Establish scalable approaches for integrating relational, vector, graph, and other enterprise data technologies.
• Define architectural standards supporting secure, reliable, and interoperable enterprise data environments.
• Evaluate emerging technologies and recommend improvements to enterprise data platforms, architectures, and processes.

• Build and manage knowledge graphs, ontologies, and metadata structures that improve data context and discovery.
• Support semantic technologies including RDF, OWL, and SPARQL.
• Develop approaches for contextual entity relationships, metadata enrichment, and semantic data integration.
• Enable structured and contextual data capabilities supporting advanced analytics, retrieval, and AI workflows.

• Develop secure, governed data pipelines and processes that produce reliable, high-quality data.
• Support automated ETL pipeline creation, scheduling, monitoring, and optimization.
• Support complex data parsing and streamlined ingestion.
• Implement schema-aligned ingestion and normalization for diverse structured and unstructured data.
• Promote repeatable, automated DataOps practices across data engineering environments.

• Establish enterprise data governance, data quality, and metadata management standards.
• Maintain provenance and end-to-end data lineage across data workflows.
• Establish policies and processes supporting trusted enterprise data.
• Implement Attribute-Based Access Control at the data layer.
• Ensure data sharing and access align with security, compliance, and zero-trust requirements.

• Provide structured and contextual data supporting AI/ML model development, analytics, and retrieval workflows.
• Support multi-model and multi-modality orchestration involving reasoning, perception, memory, and tool execution.
• Support temporal, spatial, geographic, quantitative, and computer vision AI implementations.
• Support Named Entity Recognition, contextual entity extraction, and entity resolution.
• Support multi-step retrieval and structured output generation.
• Enable deep source citation and traceable analytic outputs.
• Integrate reasoning components, retrieval systems, databases, and AI/GenAI services.

• Support vector, graph, and multi-database orchestration.
• Support air-gapped, hybrid, and on-premises operational environments.
• Support container orchestration and distributed systems management using Kubernetes.
• Support locally hosted GPU servers and hyperconverged nodes.
• Support multitenant Cross-Domain Solutions.
• Enable secure data integration and sharing across multi-enclave environments.

• Support data integration with UI, backend, and middleware environments using TypeScript, React, Python, FastAPI, LanceDB, Lance Graph, PostgreSQL, Temporal.io, Docker, and Kubernetes.
• Support in-house custom-developed applications.
• Integrate enterprise data platforms with modern applications, analytics services, retrieval systems, and AI/GenAI capabilities.
• Support solutions using modern programming languages and frameworks.

• Serve as the senior technical authority for enterprise data architecture and DataOps practices.
• Collaborate with data engineers, software engineers, AI/ML teams, analysts, and other technical stakeholders.
• Define and promote enterprise standards for data architecture, governance, quality, metadata, and integration.
• Communicate complex data architecture concepts clearly to technical and non-technical stakeholders.

• Bachelor’s degree. [Required]

• Minimum 10 years of relevant experience. [Required]
• Experience in data architecture, data engineering, or related technical disciplines. [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]
• Experience with cloud data services and secure, multi-environment data integrations. [Required]

• Familiarity with semantic technologies including RDF, OWL, and SPARQL. [Required]
• Experience with ontologies and knowledge graph design. [Required]
• Strong understanding of enterprise data architecture, data integration, and DataOps practices. [Required]
• Ability to support secure data sharing, governance, and zero-trust data requirements. [Required]
• Ability to communicate complex technical concepts clearly to non-technical stakeholders. [Required]

• 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.