Data Architect (DataOps)

Amatriot Group•Chantilly, VA

19h

About The Position

Lead the design and management of the enterprise data environment so 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, enable secure data sharing across environments, and collaborate with engineering and AI teams to maintain structured, governed, high-quality data. The role includes enterprise data architecture, semantic technologies and knowledge graphs, data pipelines and quality, and secure data sharing and access. The position supports an in-house custom-developed application involving advanced AI, GenAI, and multi-model engineering; data architecture and ETL; enterprise infrastructure and cross-domain solutions; and application development and integration.

Requirements

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.
Active TS or higher security clearance; must be SCI eligible.

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 multi-modality orchestration involving reasoning, perception, memory, and tool execution.
Implement temporal, spatial, geographic, quantitative, and computer vision AI capabilities; named entity recognition, contextual entity extraction and resolution; multi-step retrieval; structured output generation; complex data parsing; streamlined ingestion; and traceable analytic outputs with source citations.
Support vector, graph, and multi-database orchestration; automated ETL pipeline creation, scheduling, and optimization; data-layer attribute-based access control; provenance and end-to-end data lineage; and schema-aligned ingestion and normalization of structured and unstructured data.
Support air-gapped, hybrid, and on-premises environments, container orchestration and distributed systems management using Kubernetes, locally hosted GPU servers and hyperconverged nodes, and multitenant cross-domain solutions.
Develop and integrate UI, backend, and middleware components and connect reasoning components, retrieval systems, databases, and AI/GenAI services.