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

Position Overview

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.

Key Responsibilities

Enterprise Data Architecture

• 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.

Knowledge Graphs and Semantic Data

• 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.

Data Engineering and DataOps

• 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.