Data Engineering Manager

Position Summary: -

Lead sustainable data analytics and in-process quality control. Guide hands-on integration/optimization of engineering/production data (MES, SPC, FDC, EDC, RMS, OS, defects) for smart, high-yield manufacturing.

Key Responsibilities:-

1. System Integration for Data Collection

-Collaborate with Automation/CIM to integrate MES, SPC, EDC, RMS, OS, FDC, defect data.

-Ensure end-to-end digital traceability (wafer/die, BOM, equipment, lot, operator, tools, processes).

-Manage system configs, recipe levels, host/tool connectivity (SECS/GEM, OPC-UA).

-Align data automation with process/equipment ramp timelines.

2. Data Analytics & Continuous Improvement

-Build fab analytics architecture: dashboards, real-time KPIs, yield/cycle-time visibility.

-Develop predictive models for equipment health, flow optimization, yield correlation, anomaly detection.

-Enable real-time alarms, fast RCA for <1-lot impact and near-zero yield loss.

-Partner with quality/yield/engineering for actionable insights.

3. AI & Advanced Capabilities

-Lead ML, predictive analytics, Agentic AI for decision automation.

-Establish MLOps pipeline for model deployment/monitoring.

-Implement AI for AOI, yield routing, process stability.

4. Leadership & Strategy

-Build/lead team of data engineers, automation analysts, system specialists.

-Develop Data Engineering roadmap aligned with Industry 4.0/5.0.

-Foster cross-functional collaboration (IT, MFG, Engineering, Quality, global HQ).

-Ensure data governance, cybersecurity, system reliability.

Required Qualifications: -

-Bachelor's or Master's in related Engineering.

-8+ years semiconductor engineering (process preferred; equipment, yield, quality, manufacturing); 5+ years hands-on development/troubleshooting.

-Strong knowledge of process/equipment I/O, tooling, materials, facilities; process flow, Q-time, OCAP, handling, particle control mechanisms.