Engineering Data Architect

ARCTOS TECHNOLOGY SOLUTIONS•Dayton, OH

3d•$110,000 - $135,000•Onsite

About The Position

ARCTOS Technology Solutions, LLC (ARCTOS) is a fast-growing, technology-oriented small business providing aerospace, defense, and digital solutions, with offices and work sites across the United States. We’re looking for team-oriented innovators eager to tackle interesting challenges, work on important problems, and receive great benefits and employee support. ARCTOS is seeking an experienced and highly motivated engineer to join our team supporting AFRL’s Air Platform Division. This position will lead the development of a next-generation Digital Engineering Environment that integrates experimental test data, computational models, and engineering workflows into a scalable digital thread to support the development of reusable hypersonic systems. The successful candidate will work alongside experimentalists and modeling personnel and will translate engineering workflows into a robust digital ecosystem that enables data traceability, model validation, reproducibility, and engineering decision support.

Requirements

Python programming
SQL and database design
REST APIs and data integration
Version control (Git)
Data pipelines and workflow automation
Experience working with large engineering datasets
Familiarity with structured and unstructured data management
U.S. Citizenship is required
M.S. or Ph.D. in Mechanical Engineering, Aerospace Engineering, Computer Science, Systems Engineering, Data Engineering, or a related technical field.
5–10 years of experience supporting engineering analysis, scientific computing, digital engineering, simulation data management, or engineering software development.
Experience integrating experimental data with computational models.
Experience designing engineering data architectures or digital engineering environments.

Nice To Haves

Mechanical, Aerospace, Systems, or Computational Engineering background
Understanding of experimental mechanics and structural testing
Familiarity with finite element modeling and simulation workflows
Knowledge of thermal, structural, or multiphysics analyses
Appreciation for uncertainty quantification, model validation, and verification
Microsoft Azure
AWS or Google Cloud
Siemens Teamcenter
Aras Innovator
Digital Thread / Digital Engineering concepts
Engineering workflow orchestration
Knowledge graph technologies
Data lake architectures
Equally comfortable discussing finite element models and database schemas.
Curious and motivated to understand how engineers perform analyses and experiments.
Able to translate complex engineering workflows into scalable software architectures.
Comfortable working in an agile research environment where requirements evolve rapidly.
A strong communicator capable of bridging experimental, modeling, software, and program management communities.
Comfortable building prototype solutions while maintaining a long-term architectural vision.
Experience supporting DoD, aerospace, or other configuration-controlled engineering programs is highly desirable.

Responsibilities

Develop the overall architecture for a layered Digital Engineering Environment integrating test data, simulation models, metadata, workflows, and engineering knowledge.
Work directly with modeling and experimental engineers to understand existing workflows, data products, and engineering processes.
Design and implement the initial engineering data model, metadata schema, naming conventions, and version-control strategy.
Develop automated pipelines for ingesting, organizing, processing, and managing experimental and simulation data.
Establish interfaces between test data, finite element models, computational workflows, and engineering applications.
Design digital threads that preserve relationships between requirements, configurations, tests, analyses, engineering findings, and future certification evidence.
Develop prototype dashboards and engineering applications that allow users to visualize, search, and correlate engineering data.
Evaluate commercial technologies (Azure, Teamcenter, Aras, Palantir, etc.) and recommend implementation strategies aligned with long-term program objectives.
Document architecture decisions, standards, and best practices to support future system growth.