Data Architect (DataOps) | SME

Chantilly, VA, US

Actively hiring, first seen by us 2 days ago

Pay: $160,000–189,000/yearAnnual period assumed — pay source
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
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Work setting: Not stated
Employment: Full-time — employment source
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
Read the full posting

What you’ll work on

Full posting

Design and maintain enterprise data architectures across multiple data systems
Build and manage knowledge graphs, ontologies, and metadata structures
Develop secure, governed data pipelines and automated ETL processes
Establish data governance, quality, and metadata standards
Serve as senior technical authority for data architecture and DataOps

From the employer’s posting

Design and maintain enterprise data architectures integrating multiple data systems and platforms.

Build and manage knowledge graphs, ontologies, and metadata structures that improve data context and discovery.

Develop secure, governed data pipelines and processes that produce reliable, high-quality data.

Establish enterprise data governance, data quality, and metadata management standards.

Serve as the senior technical authority for enterprise data architecture and DataOps practices.

What you’ll bring

All qualifications

Core experience

Minimum 10 years of relevant experience
Active TS clearance or higher; must be able to obtain CI Polygraph
Bachelor's degree
Experience with enterprise data platforms, distributed systems, and modern pipelines
Experience with ontologies and knowledge graph design

Preferred experience

Experience supporting AI/ML teams, retrieval architectures, or vector-based search.
Experience with streaming data, real-time processing, or hybrid-cloud architectures.

Qualification wording

Minimum 10 years of relevant experience.

Active TS or higher [Required] Must be able to obtain CI Polygraph

Bachelor’s degree. [Required]

Strong experience with enterprise data platforms, distributed systems, and modern data pipelines.

Experience with ontologies and knowledge graph design.

Experience supporting AI/ML teams, retrieval architectures, or vector-based search.

Experience with streaming data, real-time processing, or hybrid-cloud architectures.

Education & alternatives

Education - Bachelor’s degree. [Required] Experience

Apply at Amatriot Group

Posting record

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Actively hiringFirst seen by us 2 days ago, still listed, and not a repost of an earlier posting.

Status in our records: Live
First seen by us: Nov 1, 20262 days ago
Recorded sightings: 4
Last seen by us: Nov 4, 2026
Employer says posted: Oct 28, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Pay

In line with similar jobs

$160K–$189K against $154K–$225K, the middle half of what similar US postings state.

From 2,958 postings in the last year. Stated ranges, not salaries paid.

Time since we first saw it

Day 2

Early. Similar jobs usually come down between day 7 and day 53.

From 7,797 similar postings that came down. Coming down is not always a hire.

Amatriot Group is hiring

21 open jobs

Open jobs by the week we first saw them, last 12 weeks.

Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

Python
TypeScript
Docker
Kubernetes
PostgreSQL
FastAPI
React

Where the posting names them

Application and Platform Integration - 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.

- 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.

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Your next step

Complete your application on amatriot.clearcompany.com. The employer’s form will show what is required.

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Has Amatriot Group posted this job before?

Yes. We’ve followed one other Data Architect (DataOps) | SME job there since November 2026. It is still up.

ChantillyStill up after 3 days, first seen Nov 1
This jobUp 3 days so far, first seen Nov 1

About the role

We file this job under Data Leadership, by the work the posting describes.

Data leaders run data teams or design how a company's data fits together. Managers hire, set priorities and answer for whether the data work pays off; architects choose the systems and the shape of the data.

Expect years of hands-on data work, experience leading people or projects, and the ability to explain tradeoffs to executives.

Unlike a senior engineer or analyst, most of a data leader's week goes to people, planning and decisions rather than building.

US government figures for computer and information systems managers

Median yearly pay, May 2025: $175,140
Expected change, 2025 to 2035: 15.8% more jobsagainst 3.5% for all jobs

The government has no code for data leaders, so these figures are for the closest group, computer and information systems managers, which includes data processing and data operations managers.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 11-3021.

Job description

View original posting ↗

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.
Promote repeatable, automated DataOps practices across data engineering environments.

Data Governance, Quality, and Metadata

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.

AI, GenAI, and Retrieval Support

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.

Data Platform and Cross-Domain Integration

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.

Application and Platform Integration

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.

Technical Leadership and Collaboration

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.

Qualifications

Education

Bachelor’s degree. [Required]

Experience

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]

Skills

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]

Preferred Qualifications

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.

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