Global Process Digital Authority - Manufacturing Assets & Maintenance

Job Description Summary

Define and own the global digital standard for maintenance management and machine data utilization across Gas Power manufacturing sites. Partners with the Global Process Engineering Authority, who owns the physical asset, controls infrastructure, and automation standards, to integrate machine data across platforms and translate it into actionable insights for asset reliability, process stability, and quality. Covers the full scope from maintenance planning and work order execution to machine program management and operational data flows.

Job Description

Key Responsibilities

Global Standard Definition & GPDA Authority

• Define and own the global digital blueprint for maintenance management and machine data utilization across Gas Power manufacturing sites. Establish the global standard and partner with local teams to adopt and implement it consistently across all sites.
• Partner with the Global Process Engineering Authority (GPEA), who owns the physical asset, controls infrastructure, sensors, and data dictionary. This role defines what is done with that data: how it is integrated across systems, how it surfaces as insights, and how it drives action at the operational level.
• Partner with the GPEA to develop, maintain, and update Automation Process Specifications (APS) and Machine Process Specifications (MPS), ensuring digital standards for data collection, maintenance execution, and machine monitoring reflect the most current engineering and automation standards across all sites.
• Partner with other process owners and Global Process Digital Authorities where machine and asset data intersects with shop floor execution, quality, and materials planning and replenishment.

• Own the digital standard for CNC program management, version control, and secure distribution to machines across sites, ensuring the right program is available at the right machine at the right time.
• Partner with the GPEA to develop the digital standard for machine data collection and own the operational intelligence: utilization, cycle times, alarms, and program execution, ensuring data is structured and surfaced in ways that drive action.
• Partner with the GPEA to develop the integration standard between machine-level systems and the manufacturing execution system, and ensure data flows reliably and is actionable at the floor level.
• Own the digital standard for maintenance planning, work order execution, and downtime tracking. Support the ongoing transition from current CMMS platforms to target-state maintenance management tools as that program evolves.
• Standardize maintenance, repair, and operations spare parts digital workflows, connecting to the Warehousing and Materials process owners for inventory positioning and replenishment.
• Lay the foundational data and process groundwork that will enable predictive maintenance capabilities over time, ensuring current implementations are structured to support that future state.
• Co-own application selection and solution design with Digital Technology and the New Technology Introduction team. Partner with the Master Data Governance team on asset master data, equipment hierarchies, and data standards that enable consistent reporting and integration across sites.

• Build and coordinate a global network of site-level process owners and subject matter experts covering both maintenance management and machine controls disciplines. Lead a formal Community of Practice to drive standard adoption, share operational learnings across sites, and prevent local workarounds from becoming embedded practice.
• Serve as the primary escalation point when sites diverge from the global standard. Monitor post-deployment usage and outcomes to identify gaps between the standard and actual maintenance and controls execution, and drive updates through a controlled change process.
• Own frontline adoption metrics for maintenance and machine data digital tools. Conduct process walks at each site before deployment to validate readiness, and develop training materials and job aids aligned to the global standard. Ensure lessons learned from each site are incorporated into the global standard so every subsequent deployment improves on the last.

• Bachelor's degree in Engineering, Manufacturing, Supply Chain, or a related technical field.
• 7+ years in manufacturing operations, maintenance engineering, or manufacturing engineering in an industrial environment, with direct accountability for process standards or operational improvement.
• Hands-on experience with computerized maintenance management systems (IBM Maximo, L2L, or equivalent) and/or CNC program management and distribution systems.
• Working familiarity with industrial automation infrastructure, including PLCs, sensor networks, and machine data collection, sufficient to partner effectively with process engineering and automation teams on data standards and integration requirements.
• Proven experience defining process standards and driving adoption across multi-site manufacturing networks.
• Experience translating operational requirements into functional specifications for technology teams.
• Strong influencing and facilitation skills; able to build alignment across maintenance, manufacturing engineering, and operations teams with varying levels of digital maturity.

• Familiarity with machine monitoring platforms and manufacturing data collection in a discrete manufacturing environment.
• Exposure to manufacturing execution system integration requirements and data flows between machine-level systems and ERP.
• Understanding of reliability engineering principles and maintenance strategy frameworks in a complex manufacturing environment.
• Experience building and enabling site-level subject matter expert or champion networks across a global manufacturing organization.
• Lean manufacturing background; familiar with total productive maintenance principles and their connection to digital maintenance systems.
• Comfortable operating at the intersection of manufacturing engineering and digital technology, and with early-stage problem definition and ambiguity.