Senior Manager – Data Management & Digital Engineering

**Senior Manager – Data Management & Digital EngineeringAutomotive Dimensional Design & StrategyPosition Summary:

The Senior Manager – Data Management, Digital Engineering & Industry 4.0 is responsible for defining and executing the digital strategy for the Dimensional Design & Strategy organization. This role leads the development, deployment, governance, and optimization of data management systems, AI-enabled solutions, Industry 4.0 technologies, statistical analysis platforms, and project management tools that support vehicle dimensional engineering, geometric quality, virtual validation, manufacturing integration, and product development.

The position serves as the organization's digital transformation leader, ensuring that engineering data, analytics, automation, and advanced technologies are leveraged to improve quality, accelerate problem resolution, enhance decision-making, and support future vehicle development.

This role partners closely with Product Development, Manufacturing Engineering, Quality, Information Technology, Data & AI organizations, and global dimensional teams to deliver scalable solutions that enable data-driven engineering excellence.

Key Responsibilities:Strategic Leadership:

• Develop and execute a multi-year digital strategy for Dimensional Design & Strategy.

• Establish a roadmap for Data Management, AI, Industry 4.0, Digital Twins, Advanced Analytics, and Engineering Automation initiatives.

• Align technology investments with business objectives, quality targets, launch readiness, and engineering productivity goals.

• Drive adoption of emerging technologies that improve dimensional performance, root cause analysis, and predictive quality management.

• Act as the executive sponsor for enterprise-wide dimensional data transformation initiatives.

Data Management & Governance:

• Own the dimensional engineering data ecosystem across product development and manufacturing.

• Establish data governance standards, data quality requirements, digital workflows, and data lifecycle management processes.

• Develop architectures that integrate:

• CAD/CAE systems

• Measurement systems

• Metrology databases

• Manufacturing systems

• Vehicle quality databases

• Product lifecycle management (PLM) environments

• Ensure traceability, accessibility, security, and compliance of engineering data.

• Lead development of enterprise dimensional data repositories and knowledge management systems.

• Define data standards supporting global engineering collaboration.

Artificial Intelligence & Advanced Analytics:

• Lead implementation of AI-enabled engineering solutions.

• Evaluate and deploy machine learning, predictive analytics, and generative AI technologies to support:

• Variation analysis

• Geometric optimization

• Root cause identification

• Defect prediction

• Quality forecasting

• Engineering knowledge retrieval

• Champion the use of enterprise AI platforms and analytical tools to increase engineering productivity and decision quality.

• Establish governance for responsible AI usage, including data security, validation, and model effectiveness.

• Identify opportunities for automated engineering insights through AI-driven analytics.

Industry 4.0 & Digital Manufacturing:

• Drive Industry 4.0 initiatives that connect dimensional engineering with manufacturing operations.

• Lead deployment of:

• Connected measurement systems

• Smart factory technologies

• Digital twins

• IoT-enabled monitoring systems

• Automated data collection architectures

• Collaborate with manufacturing and quality organizations to develop closed-loop quality systems.

• Support integration of virtual and physical validation environments.

• Identify automation opportunities that improve measurement efficiency and process control.

Statistical Analysis & Engineering Tools:

• Establish enterprise standards for statistical analysis methodologies.

• Lead deployment and governance of engineering analytics platforms.

• Develop capabilities supporting:

• Process capability analysis (Cp/Cpk)

• Measurement system analysis (MSA)

• Design of Experiments (DOE)

• Bayesian Statistics

• Regression analysis

• Multivariate analysis

• Predictive modeling

• Statistical process control (SPC)

• Ensure engineering teams have access to appropriate tools, training, and best practices.

• Promote advanced data visualization and dashboard solutions that improve decision-making.

Team Leadership:

• Build organizational capability in AI, analytics, data governance, and Industry 4.0 practices.

• Mentor technical leaders and high-potential employees.

• Foster a culture of innovation, continuous improvement, and data-driven decision-making.

• Drive competency development across the Dimensional Design & Strategy organization.