Data Architect

ARCTOS a DCS company is seeking a technical leader to establish and lead a new Integrated Data and Engineering Analytics (IDEA) team supporting a portfolio of structures and materials R&D and technology programs within the Aerospace Structures & Materials Department. The team will develop programmatic and technical architecture, analytical capabilities, digital engineering methods, and technical workforce required to transform diverse engineering and programmatic data into connected, traceable, and decision-ready knowledge.

The Team Lead will define the vision and technical architecture for this capability while building a multidisciplinary team to execute it. The successful candidate must be able to think strategically about what an integrated engineering intelligence environment should look like three to five years from now while remaining willing and able to work directly with engineers, scientists, software developers, program managers, and customers to solve today's problems.

Essential Job Functions:

Technical Vision & Architecture

Establish a three-to-five-year vision and technical roadmap for Integrated Data and Engineering Analytics.

Define and implement scalable architectures for integrating engineering data, models, digital engineering environments, programmatic information, and analytical capabilities.

Balance immediate program requirements with development of reusable organizational capabilities and new areas of business growth

Evaluate emerging technologies and identify opportunities that improve engineering analysis and program decision-making.

Engineering Data & Digital Integration

Establish methods for organizing, connecting, querying, and analyzing heterogeneous engineering information.

Define and implement approaches for metadata, provenance, traceability, configuration management, APIs, data pipelines, and authoritative data sources.

Integrate experimental data, materials information, computational models, requirements, and analytical results.

Connect engineering information with MBSE, PLM, MDAO, simulation workflows, and other digital engineering environments.

Engineering Analytics & AI/ML

Develop capabilities for extracting engineering insight from complex and heterogeneous datasets.

Apply statistical analysis, data fusion, visualization, machine learning, AI, surrogate modeling, and predictive analytics where they provide engineering value.

Combine experimental and computational information to improve models, quantify uncertainty, identify trends, and reduce technical risk.

Ensure analytical approaches maintain appropriate physical interpretation, validation, traceability, and engineering credibility.

Program Decision Analytics

Connect technical performance and engineering evidence with program cost, schedule, milestones, technical objectives, readiness, and risk.

Develop analytical approaches that allow technical and program leadership to understand how evolving engineering results affect program execution.

Identify emerging technical risks, dependencies, opportunities, and decision points.

Support quantitative, evidence-based risk management by connecting program risks to underlying engineering data, models, assumptions, and uncertainties.

Develop decision environments that allow engineers, program managers, and customers to evaluate technical and programmatic information within a common context.

Applications, Team Development & Customer Solutions

Lead development of engineering applications, dashboards, visualization environments, and decision-support tools.

Build and lead a multidisciplinary Integrated Data and Engineering Analytics team as program needs grow.

Develop strong partnerships with structures, materials, modeling and simulation, test, systems engineering, program management, and other technical organizations.

Engage government and industry customers to identify emerging engineering intelligence and digital engineering requirements.

Translate customer challenges into technical concepts, architecture, demonstrations, proposals, and executable programs.

Required Skills:

Due to the sensitivity of customer related requirements, U.S. Citizenship is required.

Bachelor's degree in aerospace engineering, mechanical engineering, computer science, computational science, systems engineering, data science, or a related technical field; advanced degree preferred.

5+ years of relevant technical experience with demonstrated technical and/or programmatic leadership.

Experience developing data architecture, software environments, digital engineering capabilities, or analytical solutions supporting engineering or scientific applications.

Experience working with complex engineering data such as experimental measurements, materials characterization, sensor data, computational simulations, or model-generated information.

Desired Skills:

Understanding of modern data architectures, metadata management, APIs, data pipelines, databases, cloud computing, and engineering data integration.

Experience integrating engineering software, models, databases, and analytical workflows.

Understanding configuration management, provenance, traceability, reproducibility, model validation, and uncertainty.

Ability to understand relationships among technical performance, program objectives, cost, schedule, uncertainty, and risk.

Demonstrated ability to communicate and lead across engineering, software/data, program management, and customer organizations.

Demonstrated ability to translate broad technical vision into practical and executable solutions.

Experience in several of the following areas is desirable: hypersonic R&D, aerospace structures and materials; experimental or computational mechanics; engineering test and measurement; finite element or multi-physics simulation; digital engineering and digital threads; MBSE/SysML; PLM; MDAO; cloud architectures; knowledge graphs and semantic architectures; data science and machine learning/artificial intelligence; model validation and uncertainty quantification; scientific computing; program decision analytics; and technical risk management.

Experience supporting Department of Defense or other government research and development programs, including familiarity with cybersecurity, controlled technical information, and data-rights requirements, is also desirable.

Physical/Working Environment:

Primary work environment is a standard office setting.

Travel - Occasional travel is expected and will be performed under the guidelines of Federal Travel Regulations (FTR) and/or Joint Travel Regulations (JTR) Occasional travel is expected and will be performed under the guidelines of Federal Travel Regulations (FTR) and/or Joint Travel Regulations (JTR).

Understanding of modern data architectures, metadata management, APIs, data pipelines, databases, cloud computing, and engineering data integration.

Experience integrating engineering software, models, databases, and analytical workflows.

Understanding configuration management, provenance, traceability, reproducibility, model validation, and uncertainty.

Ability to understand relationships among technical performance, program objectives, cost, schedule, uncertainty, and risk.

Demonstrated ability to communicate and lead across engineering, software/data, program management, and customer organizations.

Demonstrated ability to translate broad technical vision into practical and executable solutions.

Experience in several of the following areas is desirable: hypersonic R&D, aerospace structures and materials; experimental or computational mechanics; engineering test and measurement; finite element or multi-physics simulation; digital engineering and digital threads; MBSE/SysML; PLM; MDAO; cloud architectures; knowledge graphs and semantic architectures; data science and machine learning/artificial intelligence; model validation and uncertainty quantification; scientific computing; program decision analytics; and technical risk management.

Experience supporting Department of Defense or other government research and development programs, including familiarity with cybersecurity, controlled technical information, and data-rights requirements, is also desirable.