Global Supplier Quality Data Analyst

Siemens Energy, Inc.; Orlando (FL); On-site; USD 110,000 - 150,000; Full time

Benefits offered by this job

Career growth

Health benefits

Paid time off

401K match

Family benefits

Parental leave

Job summary

Siemens Energy, Inc. is seeking a skilled professional to lead quality data governance for global gas turbine supplier quality.

You will manage comprehensive data across incoming inspection, testing, defects, corrective actions, supplier audits, and compliance documents, ensuring data integrity and standardized standards. You will build KPI models to monitor defect rates, non-conformity closures, and delivery quality, performing trend and root-cause analyses to drive proactive risk management

Qualifications

• Bachelor’s degree in Quality Management, Mechanical Engineering or related field.
• Proficient in quality data processing and analytics; strong Excel, SQL and Python skills.

Responsibilities

• Manage full-cycle quality data of global gas turbine component suppliers, including inspection, testing, and corrective action documentation.
• Build KPI models and conduct trend/root-cause analysis for quality metrics.
• Operate SAP/QM and supplier quality platforms; coordinate system upgrades and data dashboards.
• Identify process deficiencies and promote standardized, digital data management in line with ISO 9001.
• Collaborate with R&D, manufacturing, procurement and IT on digital optimization projects.

Skills

Quality data governance

SQL

Python

SAP/QM

Data modeling

Dashboard development

ISO 9001

English communication

Education

Bachelor’s degree in Quality Management or related

Tools

SAP/QM

Excel

Job description

Siemens Energy, Inc. is seeking a skilled professional to lead quality data governance for global gas turbine supplier quality.

You will manage comprehensive data across incoming inspection, testing, defects, corrective actions, supplier audits, and compliance documents, ensuring data integrity and standardized standards. You will build KPI models to monitor defect rates, non-conformity closures, and delivery quality, performing trend and root-cause analyses to drive proactive risk management