Process Engineer Technician, Service Distribution

Tesla is looking for a highly motivated, goal-oriented Process Engineer Technician to contribute to implementing Industrial Engineering and Lean Principles for all processes at our Parts Distribution Centers in North America. Through a detailed understanding of the Distribution network's objectives and extensive collaboration with internal and external teams, the Process Technician will help improve processes to increase productivity and quality. Tesla is a demanding and fast-paced environment where you will work on a critical subsystem of an incredibly exciting product.

• Assist process engineers in developing and deploying efficient processes to optimize the overall operation regarding associates, equipment, material requirements, and information to achieve an optimal state

• Collaborate with stakeholder teams like Warehouse Operations, Facilities Engineering, IT Application Developers, Logistics, Safety, Material Planning, MHE Vendors, and 3PLs

• Collaborate with management and cross-functional support teams to identify process constraints and execute solutions accordingly

• Ability to create multi-process workstation layouts, standardized work, staffing, and process maps to execute a successful rebalance of workstations as needed

• Ability to prepare diagrams to illustrate workflow, product routing, floor layouts, material handling, and equipment utilization

• Ability to collect statistical cycle time studies, analyze data, and optimize processes

• Working knowledge of 5S principles, Work Measurement, JIT, Continuous Improvement, and Problem-Solving Methods

• Understands the definition of Cycle Time, and Lean concepts

• Able to work under pressure while managing competing demands and tight deadlines

• Exemplary verbal and written communication skills

• Advanced skills in Microsoft Word, Excel, and PowerPoint

• Ability to work well with others in a collaborative team environment

• Comfortable in an environment with unstructured, incomplete, and ambiguous data