Data Architect - Team Lead

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Full-Time Beavercreek, OH, US

6 days ago Requisition

Salary Range: $ To $ Annually

ARCTOS Technology Solutions, LLC

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 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 architectures, 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.

• 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.
• 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.
• 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.

• 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.

• Lead development of engineering applications, dashboards, visualization environments, and decision-support tools.
• Build and lead a multidisciplinary Integrated Data and Engineering…