ANALYST II ENTERPRISE SYSTEMS

Job Overview

Support Engineering AI initiatives through use-case development, user acceptance testing (UAT), user training, adoption, and support. Apply Creo, PDMLink, and engineering-process expertise to validate solutions and improve engineering productivity.

Job Requirements

• AI use cases: Identify, document, prioritize, and validate engineering AI use cases in partnership with engineering users and stakeholders.
• User acceptance testing: Prepare representative test scenarios, execute UAT, document results, track defects, and coordinate issue resolution.
• Training and user support: Create user guides and learning materials, conduct training and demonstrations, support users, and capture structured feedback.
• Engineering systems expertise: Apply hands-on Creo, PDMLink, and mechanical-engineering process knowledge to assess solution fit, usability, and business relevance.
• Adoption and change support: Support communications, onboarding, readiness activities, feedback loops, and adoption campaigns for Engineering AI capabilities.
• Productivity improvement: Support process optimization, establish baseline measures, and track productivity and user-experience benefits.
• Collaboration and documentation: Work with DES, business units, technology teams, and solution providers while maintaining clear use-case, test, training, and support documentation.

Required Qualifications and Experience:

• Education: Bachelor’s degree in Mechanical Engineering or an equivalent engineering discipline.
• Experience: 3 to 5 years of relevant experience in mechanical engineering, engineering systems, digital engineering, or application support.
• Technical expertise: Hands-on experience with Creo, PDMLink/Windchill, and mechanical-engineering or product-development processes.
• Testing and validation: Experience preparing test scenarios, conducting UAT, documenting results, and coordinating defect resolution.
• Training and support: Ability to create clear user documentation, deliver training, support users, and translate feedback into practical improvements.
• Improvement methods: Exposure to Lean, process mapping, automation, analytics, or similar productivity-improvement methods.
• Core skills: Strong problem-solving, communication, documentation, training, and cross-functional collaboration skills.

Preferred Qualifications:

• Exposure to Engineering AI, digital engineering, AI-assisted design, or engineering automation.
• Experience supporting pilots, proof-of-concepts, application rollouts, or adoption initiatives.
• Familiarity with engineering data, design-review workflows, and enterprise application support processes.

Success Measures:

• Delivery: Use cases and UAT activities completed with clear documentation, traceable results, and stakeholder alignment.
• Adoption: Users trained, supported, and enabled to apply Engineering AI capabilities effectively.
• Quality: Issues, feedback, and improvement actions documented and progressed through appropriate resolution channels.
• Impact: Engineering productivity and user-experience improvements identified, measured, and demonstrated.

Career Path:

Potential progression into Engineering Systems SME, Engineering AI Product Analyst, Product Owner, or Digital Engineering lead roles within DES.