Senior Data Architect, DataOps & Knowledge Graphs

The-Amatriot-Group-1; Chantilly (VA); On-site; USD 160,000 - 189,000; Full time

Job summary

The Data Architect (DataOps) will lead the design, integration, governance, and optimization of the enterprise data environment. You will work to ensure data is accurate, connected, secure, and ready for advanced analytics and AI workloads.

Responsibilities include building knowledge graphs, managing metadata, and enabling secure data sharing across multi-environment deployments. Collaboration with engineering, analytics, and AI teams is essential.

Qualifications

• Minimum 10 years of relevant experience in data architecture and related disciplines.
• Experience with enterprise data platforms, distributed systems, and modern data pipelines.
• Proven knowledge of metadata management, data lineage, and data governance.
• Experience with cloud data services and secure, multi-environment data integrations.
• Familiarity with semantic technologies including RDF/OWL/SPARQL is required.

Responsibilities

• Design and maintain enterprise data architectures across multiple systems.
• Define standards for secure, interoperable data environments and governance.
• Evaluate emerging technologies to improve data platforms and processes.
• Collaborate with data engineers, software teams, and AI groups to enable analytics and retrieval workflows.

Skills

Data Architecture

DataOps

Knowledge Graphs

Semantic Technologies

RDF/OWL/SPARQL

Enterprise Data Governance

Security & Zero-Trust

Stakeholder Communication

Tools

LanceDB

Lance Graph

PostgreSQL

Temporal.io

Docker

Kubernetes

Python

TypeScript

React

Job description

The Data Architect (DataOps) will lead the design, integration, governance, and optimization of the enterprise data environment. You will work to ensure data is accurate, connected, secure, and ready for advanced analytics and AI workloads.

Responsibilities include building knowledge graphs, managing metadata, and enabling secure data sharing across multi-environment deployments. Collaboration with engineering, analytics, and AI teams is essential.