Responsible for designing, developing, and maintaining business intelligence and analytics applications that embed product quality intelligence into key lifecycle quality decisions. Planned, organized, and coordinated data and reporting activities to transform manufacturing, test, and field information into actionable insights. Collaborates with product quality, engineering, reliability, test, supply chain, and field quality teams to monitor performance, detect early risk indicators, and support root cause analysis (RCA). Responsible for operationalizing quality KPIs (yield, defect rates, fallout, escapes, reliability indicators, and customer-reported failure trends) through standardized datasets, dashboards, and analytics-ready models. Additionally, proposes and contributes to advanced analytics and machine learning opportunities to improve quality prediction, classification, and anomaly detection. Design and delive r BI and analytics solutions providing visibility into product quality performance across the lifecycle. Translate manufacturing, test, supply chain, and field dat a into analytics-ready datasets through effective data modeling and data integration collaboration. Define, standardize, and operationaliz e product quality KPIs (yield, defect rates, fallout, escapes, reliability indicators, and customer failure trend metrics). Develop dashboards, reports, and visual analytic s aligned with quality, engineering, and operations user needs. Proactively analyze trends and anomalies. Partner with cross-functional teams. Support root cause analysis (RCA ) and systemic issue identification using structured data exploration. Collaborate with global data/development team. Experience: Bachelor's degree in Information Systems, Computer Science, Engineer ing, or a related technical field. Minimum 2 ye ars of experience in Business Intelligence, Analytics, or data-driven roles. Experience supporting or working with in product quality, manufacturing quality, reliability engineering, t est, or field qual ity environment s, or proven experience analyzing quality-related data. Strong understand ing of data warehousing concepts, relational data modeling, and analytical best practices. Practical experience wi th ETL and data integration proces ses, including requirements definition and transformation validation (partnering with ETL developers). Proficiency in SQL and data exploration; experience wi th Pyt hon is a plus. Experience developi ng reports, dashboards, and visual analyt ics using tools such as Power BI or Business Objects. Ability to translate business and quality requirements into scalable analytical solutions. Strong analytical, problem-solving, and structured thinking skills. Experience working wi th global or distributed teams. Clear communication skills with both technical and non-technical audiences; ability to brid ge engineering, quality, and data scie nce perspectives.
