Senior Digital Data Architect

Modern Technology Solutions Inc•Alexandria, VA

1h•Hybrid

About The Position

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.

Requirements

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.

Nice To Haves

Cloud architecture experience (Azure,AWS).
Familiarity with ontology development (RDF/OWL) and data governance tools.
Familiarity with containerized deployments (Docker/Kubernetes).

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