Data Architect (DataOps)

Amatriot Group•Chantilly, VA

12h

About The Position

The Data Architect (DataOps) will lead the design and management of an enterprise data environment so information is accurate, connected, and ready to support advanced analytics and AI. The role integrates traditional databases, modern data platforms, and semantic and knowledge-graph technologies to support data discovery and decision-making. It establishes data standards, enables secure data sharing across environments, and collaborates with engineering and AI teams to maintain structured, governed, high-quality data. The position also involves advanced AI, GenAI, and multi-model engineering capabilities, including coordinating reasoning, perception, memory, and tool execution; implementing temporal, spatial, geographic, quantitative, and computer-vision AI; and supporting entity extraction, retrieval, structured output generation, parsing, ingestion, and traceable analytic outputs. The work spans data architecture and ETL, enterprise infrastructure and cross-domain solutions, and application development and integration. The described environment includes air-gapped, hybrid, and on-premises operations; Kubernetes-based container orchestration; locally hosted GPU servers and hyperconverged nodes; and multitenant Cross-Domain Solutions. The application is an in-house custom-developed application.

Requirements

Active TS or higher security clearance; must be able to obtain a CI Polygraph.
Bachelor’s degree.
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.
Experience with metadata management, data lineage, and data governance processes.
Familiarity with semantic technologies including RDF, OWL, and SPARQL; ontologies; and knowledge graph design.
Knowledge of cloud data services and secure data integrations across multiple environments.
Ability to communicate complex technical concepts clearly to non-technical stakeholders.
Familiarity with modern programming languages and frameworks; the application and integration technologies named include 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.
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.
Support multi-model and multimodality orchestration involving reasoning, perception, memory, and tool execution.
Implement temporal, spatial, geographic, quantitative, and computer-vision AI capabilities.
Support named entity recognition, contextual entity extraction and resolution, multi-step retrieval, structured output generation, complex data parsing, streamlined ingestion, and traceable analytic outputs.
Orchestrate vector, graph, and multi-database systems.
Create, schedule, and optimize automated ETL pipelines.
Implement attribute-based access control at the data layer and track provenance and end-to-end data lineage.
Ingest and normalize diverse structured and unstructured data in alignment with schemas.
Work with air-gapped, hybrid, and on-premises environments, Kubernetes, locally hosted GPU servers, hyperconverged nodes, and multitenant Cross-Domain Solutions.
Develop and integrate UI, backend, and middleware components, including reasoning components, retrieval systems, databases, and AI/GenAI services.