Overview
On Site
Full Time
Skills
System Integration
Continuous Improvement
Data Architecture
JSON
Systems Architecture
Data Integration
Data Flow
Storage
NoSQL
Amazon DynamoDB
Caching
Performance Tuning
API
React.js
JavaScript
GraphQL
Testing
Debugging
Optimization
Usability
Accessibility
Clinical Data Management
Open Source
Scripting
Configuration Management
Documentation
Ontologies
Modeling
Professional Development
Management
Computer Science
Data Science
Systems Engineering
Software Development
Data Engineering
Information Systems
Data Modeling
Extract
Transform
Load
Python
Flask
Django
Database
MongoDB
Neo4j
SysML
Semantics
Data Structure
RESTful
Data Validation
Version Control
Docker
Soft Skills
Communication
Collaboration
Teamwork
Analytical Skill
Cloud Computing
Amazon Web Services
Microsoft Azure
Ontology Engineering
Resource Description Framework
OWL
Data Governance
Workflow
Orchestration
Ansys
Security Clearance
Job Details
Software & Data Systems Developer
MTSI is seeking a and Data Systems Developer and Engineer to support the design, implementation, and maintenance of a Canonical Data Model (CDM) that integrates structured, semi-structured, and model-based data sources.
The ideal candidate has hands-on experience developing and maintaining data pipelines, data models, and APIs, with an interest in advancing enterprise and model-based systems integration. This role emphasizes technical implementation, collaboration, and continuous improvement of data architecture standards under senior technical guidance.
Location: In person at Mark Center, Alexandria, VA/Hybrid (TBD)
Employment Type: Full-Time
Key Responsibilities
Support Ontology/Canonical Data Model (CDM) Development
Assist in designing and implementing a scalable CDM using Python and Pydantic.
Develop and maintain data schemas and standards for interoperability across structured (tabular), unstructured (JSON/API), and MBSE (SysML, LML) data sources.
Contribute to documentation, version control, and evolution of CDM components.
Work with senior architects to align CDM structures with broader ontology and system architecture goals.
ETL Development and Data Integration
Develop and maintain ETL pipelines integrating data from multiple systems.
Implement data validation, quality checks, and logging frameworks (e.g., Pandera).
Support automation and monitoring of routine data flows.
Troubleshoot data ingestion or transformation issues and propose solutions.
Database and Storage Implementation
Implement and maintain databases using NoSQL (MongoDB, DynamoDB) and graph technologies (Neo4j).
Optimize queries and data retrieval for analytical and model-driven workflows.
Participate in indexing, caching, and performance tuning under senior guidance.
Web Interface and API Development
Contribute to backend and API development (FastAPI/Django).
Support front-end integration with frameworks such as React or Next.js.
Implement and test RESTful or GraphQL endpoints for CDM data access.
Assist with testing, debugging, and optimization of both front-end and backend components to ensure reliable performance across environments.
Support integration of interactive features such as search, filtering, and model navigation to improve usability and accessibility of data assets.
Model Orchestration and Integration
Support integration of CDM data with model orchestration tools such as Ansys ModelCenter or open-source equivalents.
Assist in developing scripts and frameworks for orchestrating analytical workflows, simulation models, and design studies.
Maintain traceability between MBSE environments, analytical results, and enterprise data repositories.
Contribute to model execution pipelines and configuration management tasks.
Documentation
Support the development and implementation of enterprise data strategies.
Align data models with ontology and semantic modeling practices (RDF, OWL).
Document processes, schemas, and data standards for data sharing and reuse.
Collaboration and Professional Development
Work closely with senior architects, systems engineers, and data scientists on integrated data initiatives.
Participate in code reviews and collaborative design sessions.
Continue learning and contributing to best practices in data modeling and management.
Required Qualifications
Education: Bachelor's in Computer Science, Data Science, Systems Engineering, or a related field.
Experience:
2-5 years of experience in software development, data engineering, or information systems.
Hands-on experience with data modeling and ETL pipeline development.
Strong Python programming skills, particularly with Pydantic, FastAPI/Flask/Django.
Experience with databases such as MongoDB and Neo4j.
Understanding of MBSE concepts (SysML, UAF) and semantic data structures.
Technical Skills:
Proficiency in RESTful API design and implementation.
Working knowledge of data validation and version control.
Familiarity with containerized environments (Docker).
Soft Skills:
Strong communication and teamwork skills.
Analytical and solution-oriented mindset.
Willingness to learn and adapt in a multidisciplinary environment.
Preferred Qualifications
Experience with cloud platforms (AWS, Azure).
Exposure to ontology development (RDF/OWL) and data governance.
Familiarity with workflow orchestration tools (Ansys ModelCenter, Airflow, Prefect).
Experience integrating MBSE tools with data services.
Security Clearance Requirements
Preferred Top Secret / Top Secret Eligibility
#LI-BG1
MTSI is seeking a and Data Systems Developer and Engineer to support the design, implementation, and maintenance of a Canonical Data Model (CDM) that integrates structured, semi-structured, and model-based data sources.
The ideal candidate has hands-on experience developing and maintaining data pipelines, data models, and APIs, with an interest in advancing enterprise and model-based systems integration. This role emphasizes technical implementation, collaboration, and continuous improvement of data architecture standards under senior technical guidance.
Location: In person at Mark Center, Alexandria, VA/Hybrid (TBD)
Employment Type: Full-Time
Key Responsibilities
Support Ontology/Canonical Data Model (CDM) Development
Assist in designing and implementing a scalable CDM using Python and Pydantic.
Develop and maintain data schemas and standards for interoperability across structured (tabular), unstructured (JSON/API), and MBSE (SysML, LML) data sources.
Contribute to documentation, version control, and evolution of CDM components.
Work with senior architects to align CDM structures with broader ontology and system architecture goals.
ETL Development and Data Integration
Develop and maintain ETL pipelines integrating data from multiple systems.
Implement data validation, quality checks, and logging frameworks (e.g., Pandera).
Support automation and monitoring of routine data flows.
Troubleshoot data ingestion or transformation issues and propose solutions.
Database and Storage Implementation
Implement and maintain databases using NoSQL (MongoDB, DynamoDB) and graph technologies (Neo4j).
Optimize queries and data retrieval for analytical and model-driven workflows.
Participate in indexing, caching, and performance tuning under senior guidance.
Web Interface and API Development
Contribute to backend and API development (FastAPI/Django).
Support front-end integration with frameworks such as React or Next.js.
Implement and test RESTful or GraphQL endpoints for CDM data access.
Assist with testing, debugging, and optimization of both front-end and backend components to ensure reliable performance across environments.
Support integration of interactive features such as search, filtering, and model navigation to improve usability and accessibility of data assets.
Model Orchestration and Integration
Support integration of CDM data with model orchestration tools such as Ansys ModelCenter or open-source equivalents.
Assist in developing scripts and frameworks for orchestrating analytical workflows, simulation models, and design studies.
Maintain traceability between MBSE environments, analytical results, and enterprise data repositories.
Contribute to model execution pipelines and configuration management tasks.
Documentation
Support the development and implementation of enterprise data strategies.
Align data models with ontology and semantic modeling practices (RDF, OWL).
Document processes, schemas, and data standards for data sharing and reuse.
Collaboration and Professional Development
Work closely with senior architects, systems engineers, and data scientists on integrated data initiatives.
Participate in code reviews and collaborative design sessions.
Continue learning and contributing to best practices in data modeling and management.
Required Qualifications
Education: Bachelor's in Computer Science, Data Science, Systems Engineering, or a related field.
Experience:
2-5 years of experience in software development, data engineering, or information systems.
Hands-on experience with data modeling and ETL pipeline development.
Strong Python programming skills, particularly with Pydantic, FastAPI/Flask/Django.
Experience with databases such as MongoDB and Neo4j.
Understanding of MBSE concepts (SysML, UAF) and semantic data structures.
Technical Skills:
Proficiency in RESTful API design and implementation.
Working knowledge of data validation and version control.
Familiarity with containerized environments (Docker).
Soft Skills:
Strong communication and teamwork skills.
Analytical and solution-oriented mindset.
Willingness to learn and adapt in a multidisciplinary environment.
Preferred Qualifications
Experience with cloud platforms (AWS, Azure).
Exposure to ontology development (RDF/OWL) and data governance.
Familiarity with workflow orchestration tools (Ansys ModelCenter, Airflow, Prefect).
Experience integrating MBSE tools with data services.
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.