Product & Program Structure Transformation Manager

Ford Pro; Dearborn (MI); On-site; USD 110,000 - 150,000; Full time

Job summary

Ford Pro is seeking a Program and Product Structure Transformation Manager to lead initiatives in reshaping product and program data. This role offers an exciting opportunity to innovate and drive data transformation across the organization, involving collaboration with diverse teams and leveraging analytical expertise to define the future of data management.

Qualifications

• Deep knowledge of product and program structures.
• Strong analytical skills required.
• Proven experience with data transformation.

Responsibilities

• Analyze the current data landscape.
• Define foundational elements and map data to a future state.
• Collaborate with cross-functional teams for execution at scale.

Skills

Analytical abilities

Data management tools

Data transformation experience

Job description

We are the movers of the world and the makers of the future. We get up every day, roll up our sleeves and build a better world -- together. At Ford, we’re all a part of something bigger than ourselves. Are you ready to change the way the world moves?

Product Development uses design thinking & user experience methods to deliver breakthrough products and services that delight our customers. We bring innovative, exciting, and sustainable ideas to life. We have opportunities around the world for you to contribute to advancements in autonomy, electrification, smart mobility technologies, and more!

In this position...
We are looking for a Program and Product Structure Transformation Manager to drive a company-wide initiative reshaping our product and program data landscape. This role offers a unique opportunity to define the future of Ford's product and program data. The ideal candidate will possess deep knowledge of product and program structures, strong analytical abilities, proven data transformation experience, and a solid understanding of data management tools. Responsibilities include analyzing the current data landscape, defining foundational elements, mapping data to a future state, developing translation logic, and collaborating with cross-functional teams to execute data transformation at scale.