Data Analyst Supplier Quality Engineering (Integrated Supply Chain)

Key Responsibilities

• Build and maintain visual dashboards that provide clear, actionable insights into supplier quality performance, risks, trends, and corrective action effectiveness.
• Translate complex, multi-source datasets into intuitive, decision-ready visualizations for SQE leadership and stakeholders.
• Work closely with the current SQE digitization team to understand existing data flows, reporting workflows, and analytics requirements.
• Align new analytics solutions with established SQE processes, standards, and governance models.
• Develop, maintain, and automate data pipelines and workflows for Supplier Quality Engineering using Alteryx (or equivalent ETL tools).
• Ensure data accuracy, repeatability, scalability, and timely refresh of SQE analytics.
• Document data flows, transformations, and KPI definitions to support standardization and continuity.
• Create interactive dashboards using Tableau, Power BI, React-based dashboards, or equivalent tools.
• Create analytics that support supplier quality decision-making, including performance monitoring, risk identification, and continuous improvement initiatives.
• Optimize dashboards for usability, performance, and stakeholder adoption.
• Perform ad-hoc data analysis to support SQE initiatives, leadership requests, and supplier reviews.
• Present findings, trends, and insights to SQE stakeholders and leadership in a clear, concise, and business-focused manner.
• Support governance reviews, operational reviews, and continuous improvement discussions with data-backed insights.

Required Qualifications & Experience

• Bachelor’s degree in Data Science, Statistics, Engineering, or a related field.
• Hands-on experience with Alteryx or comparable ETL/data preparation tools for workflow automation and analytics enablement.
• Experience with data visualization tools such as Tableau and/or Power BI (experience with React-based dashboards is a plus).
• Strong proficiency in SQL for data extraction, transformation, and analysis.
• Experience working with large, complex datasets from multiple systems.
• Strong problem-solving, communication, and stakeholder management skills, with the ability to translate data into meaningful business insights.