Staff Technical Program Manager

Bachelor's Degree in Computer Science or “STEM” Majors (Science, Technology, Engineering and Math) with 8 to 12 years of experience.

Significant technical experience along with established leadership credentials across disciplines and functions within IT program/product areas.

Prior SAP ERP implementation experience

Bring good understanding of SAP S/4HANA concepts, process simplifications, and future-state capabilities

Demonstrated functional / technical experience in key SAP modules including PM, QM and PP

Excellent program management skills; effectively mitigating risks to keep project schedules on-time, on-budget and with quality deliverables

Passion for solving business problems using ERP and a passion for emerging technologies such as AI. Innovates and integrates new processes and/or technology to significantly add value to GE Vernova. Proactively learns new solutions and processes to address seemingly unanswerable problems.

Ability to understand, process, and interpret data to extract insights and communicate findings effectively to support business decisions.

Experience with ERP processes in Sourcing and Warehouse environments and an understanding of ERP integrations with other satellite systems.

Excellent influencing, interpersonal and communications skills (both written and verbal) with all levels of an organization. Influences and energizes others toward the common vision and goal.

Maintains excitement for a process and drives to new directions of meeting the goal even when odds and setbacks render one path impassable.

Strong global team player. Self-starter - requires minimal direction to accomplish goals.

Strong track record of understanding and interest in current and emerging technologies and / or industry activities.

Ability to manage third party partner teams to drive successful business outcomes.

Proactively engages with cross-functional teams to resolve issues and design solutions using critical thinking and analysis skills and best practices. Finds important patterns in seemingly unrelated information.