Overview
On Site
Full Time
Skills
Workflow
JSON
Change Control
Systems Architecture
ETL Architecture
Data Integration
Scalability
Database
Amazon DynamoDB
Graph Databases
Database Design
Caching
Storage
React.js
JavaScript
RESTful
GraphQL
API
Clinical Data Management
Orchestration
Ansys
Open Source
Interfaces
Data Flow
Analytical Skill
Configuration Management
Data Structure
Ontologies
Modeling
Data Quality
Organizational Architecture
Regulatory Compliance
Git
Continuous Delivery
GitHub
Jenkins
DevOps
GitLab
Continuous Integration
Testing
Collaboration
Confluence
JIRA
Documentation
Computer Science
Data Science
Systems Engineering
Software Development
Data Architecture
Leadership
Extract
Transform
Load
Management
Python
Flask
Django
NoSQL
MongoDB
Neo4j
SysML
Semantics
IT Management
System Integration
Data Modeling
Microservices
Soft Skills
Communication
Mentorship
Stakeholder Management
Technical Direction
Cloud Architecture
Microsoft Azure
Amazon Web Services
Ontology Engineering
Resource Description Framework
OWL
Data Governance
Docker
Kubernetes
Security Clearance
Job Details
Senior Digital Data Architect
MTSI is seeking a Senior Digital Data Architect to lead the design, implementation, and evolution of a Canonical Data Model (CDM) that integrates structured, semi-structured, and model-based data sources. The ideal candidate has a proven track record of architecting and managing enterprise-scale data systems, building robust ETL frameworks, and deploying data access interfaces that support knowledge discovery across diverse domains.
This role requires a strategic thinker who can balance technical execution with architectural foresight, guiding teams and shaping data standards that enable interoperability across systems engineering and analytical workflows.
Location: In person at Mark Center, Alexandria, VA/Hybrid (TBD)
Employment Type: Full-Time
Key Responsibilities
Architect and Oversee the Ontology/Canonical Data Model (CDM):
Lead the end-to-end design of a scalable CDM using Python and Pydantic.
Define modeling standards, governance, and interoperability strategies across structured (tabular), unstructured (JSON/API), and MBSE (SysML, LML) data sources.
Establish versioning, change control, and extensibility practices for CDM evolution.
Help define unified ontology for system of system architecture
Lead ETL Architecture and Data Integration:
Architect and manage ETL pipelines integrating data from multiple enterprise systems.
Oversee data quality, lineage, and validation standards using tools like Pandera.
Design for scalability, automation, and operational monitoring.
Database and Storage Strategy:
Define storage architectures using NoSQL (MongoDB, DynamoDB) and graph databases (Neo4j).
Optimize database design for query performance and relationship-heavy data.
Guide decisions on indexing, caching, and hybrid storage strategies.
Web Interface and API Enablement:
Direct the design and development of a web interface for querying and managing CDM data.
Lead integration of backend APIs (FastAPI/Django) and front-end frameworks (React/Next.js).
Promote best practices in RESTful and GraphQL API design.
Model Orchestration and Integration:
Lead the integration of the CDM with model orchestration tools such as Ansys ModelCenter, or open-source alternatives.
Develop frameworks for orchestrating analytical flows, simulation models, and design studies using standardized interfaces.
Ensure interoperability between MBSE environments, analytical models, and enterprise data repositories.
Collaborate with systems engineers to implement automated data flows and traceability between system models and analytical results.
Support model execution pipelines and configuration management across engineering tools and simulation environments.
Digital Data Leadership:
Develop and champion enterprise and digital data strategies.
Align data structures with ontologies and semantic modeling standards (RDF, OWL).
Mentor teams on data architecture principles and reusable data design.
Collaboration & Mentorship:
Serve as the technical authority across cross-functional teams.
Mentor mid-level engineers in data modeling, ETL design, and data quality practices.
Ensure solutions align with organizational architecture and compliance standards.
Using tools such as Git, GitHub, or GitLab to maintain high code quality and consistency.
Support the setup, configuration, and maintenance of CI/CD pipelines (e.g., GitHub Actions, Jenkins, Azure DevOps, or GitLab CI) to automate testing, deployment, and integration processes.
Utilize collaboration tools like Confluence, Jira, and SharePoint to manage tasking and documentation
Required Qualifications:
Education: Bachelor's or Master's in Computer Science, Data Science, Systems Engineering, or related field.
Experience:
5+ years in software development, data architecture, or enterprise data systems.
Proven leadership in designing and deploying large-scale data systems.
Strong experience architecting ETL frameworks and managing production data pipelines.
Deep proficiency with Python, Pydantic, FastAPI/Flask/Django, NoSQL (MongoDB), and Neo4j.
Understanding of MBSE concepts (SysML,UAF) and semantic data modeling.
Technical Leadership:
Expertise in systems integration, version-controlled data modeling, and microservice architectures.
Demonstrated ability to lead cross-disciplinary teams.
Soft Skills:
Exceptional communication, mentorship, and stakeholder management skills.
Strategic thinker capable of setting technical direction and delivering scalable systems.
Preferred Qualifications
Cloud architecture experience (Azure,AWS).
Familiarity with ontology development (RDF/OWL) and data governance tools.
Familiarity with containerized deployments (Docker/Kubernetes).
Security Clearance Requirements
Preferred Top Secret / Top Secret Eligibility
#LI-BG1
MTSI is seeking a Senior Digital Data Architect to lead the design, implementation, and evolution of a Canonical Data Model (CDM) that integrates structured, semi-structured, and model-based data sources. The ideal candidate has a proven track record of architecting and managing enterprise-scale data systems, building robust ETL frameworks, and deploying data access interfaces that support knowledge discovery across diverse domains.
This role requires a strategic thinker who can balance technical execution with architectural foresight, guiding teams and shaping data standards that enable interoperability across systems engineering and analytical workflows.
Location: In person at Mark Center, Alexandria, VA/Hybrid (TBD)
Employment Type: Full-Time
Key Responsibilities
Architect and Oversee the Ontology/Canonical Data Model (CDM):
Lead the end-to-end design of a scalable CDM using Python and Pydantic.
Define modeling standards, governance, and interoperability strategies across structured (tabular), unstructured (JSON/API), and MBSE (SysML, LML) data sources.
Establish versioning, change control, and extensibility practices for CDM evolution.
Help define unified ontology for system of system architecture
Lead ETL Architecture and Data Integration:
Architect and manage ETL pipelines integrating data from multiple enterprise systems.
Oversee data quality, lineage, and validation standards using tools like Pandera.
Design for scalability, automation, and operational monitoring.
Database and Storage Strategy:
Define storage architectures using NoSQL (MongoDB, DynamoDB) and graph databases (Neo4j).
Optimize database design for query performance and relationship-heavy data.
Guide decisions on indexing, caching, and hybrid storage strategies.
Web Interface and API Enablement:
Direct the design and development of a web interface for querying and managing CDM data.
Lead integration of backend APIs (FastAPI/Django) and front-end frameworks (React/Next.js).
Promote best practices in RESTful and GraphQL API design.
Model Orchestration and Integration:
Lead the integration of the CDM with model orchestration tools such as Ansys ModelCenter, or open-source alternatives.
Develop frameworks for orchestrating analytical flows, simulation models, and design studies using standardized interfaces.
Ensure interoperability between MBSE environments, analytical models, and enterprise data repositories.
Collaborate with systems engineers to implement automated data flows and traceability between system models and analytical results.
Support model execution pipelines and configuration management across engineering tools and simulation environments.
Digital Data Leadership:
Develop and champion enterprise and digital data strategies.
Align data structures with ontologies and semantic modeling standards (RDF, OWL).
Mentor teams on data architecture principles and reusable data design.
Collaboration & Mentorship:
Serve as the technical authority across cross-functional teams.
Mentor mid-level engineers in data modeling, ETL design, and data quality practices.
Ensure solutions align with organizational architecture and compliance standards.
Using tools such as Git, GitHub, or GitLab to maintain high code quality and consistency.
Support the setup, configuration, and maintenance of CI/CD pipelines (e.g., GitHub Actions, Jenkins, Azure DevOps, or GitLab CI) to automate testing, deployment, and integration processes.
Utilize collaboration tools like Confluence, Jira, and SharePoint to manage tasking and documentation
Required Qualifications:
Education: Bachelor's or Master's in Computer Science, Data Science, Systems Engineering, or related field.
Experience:
5+ years in software development, data architecture, or enterprise data systems.
Proven leadership in designing and deploying large-scale data systems.
Strong experience architecting ETL frameworks and managing production data pipelines.
Deep proficiency with Python, Pydantic, FastAPI/Flask/Django, NoSQL (MongoDB), and Neo4j.
Understanding of MBSE concepts (SysML,UAF) and semantic data modeling.
Technical Leadership:
Expertise in systems integration, version-controlled data modeling, and microservice architectures.
Demonstrated ability to lead cross-disciplinary teams.
Soft Skills:
Exceptional communication, mentorship, and stakeholder management skills.
Strategic thinker capable of setting technical direction and delivering scalable systems.
Preferred Qualifications
Cloud architecture experience (Azure,AWS).
Familiarity with ontology development (RDF/OWL) and data governance tools.
Familiarity with containerized deployments (Docker/Kubernetes).
Security Clearance Requirements
Preferred Top Secret / Top Secret Eligibility
#LI-BG1
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.