Building Manufacturing Data Assets:
- Start building MFG data Assets including MFG suppliers, Items and Customers as well.
- Collaborate with arch team for Building a new schema in DWH with MFG Data assets then data view from MFG.
- Building a quality dimension and measurement for the MFG Data pipeline.
Data Strategy & Business Partnership:
- Act as a trusted advisor to manufacturing and operations leadership by translating business needs into analytical solutions.
- Collaborate cross-functionally to define KPIs, reporting requirements, and success metrics.
- Provide root cause analysis and insights to address key business questions and drive operational efficiency.
Data Collection, Governance & Integration:
- Lead data acquisition, validation, and integration across systems.
- Ensure high data quality, consistency, and accessibility through robust governance practices.
- Champion the use of both structured and unstructured data across functions.
Production Analytics:
- Monitor the full assembly process to assess part usage, assembly time per model, and takt time.
- Track and analyze OEE (Overall Equipment Effectiveness) at the station level and identify downtime causes (e.g., material shortages, equipment failures).
- Develop dashboards for daily throughput, bottleneck detection, and station-level performance.
Procurement & P2P Cycle Analytics:
- Track the full PR-to-PO-to-Invoice lifecycle to detect delays in procurement and part availability.
- Analyze supplier delivery performance,lead times, invoice match accuracy, and customs clearance timelines.
- Build Power BI dashboards to monitor inbound logistics, warehouse availability, and procurement cycle efficiency.
Quality Management & Traceability:
- Monitor FPY (First Pass Yield), rework rates, and defect trends across lines, shifts, and suppliers.
- Trace defects and warranty claims back to specific VINs, batches, or production steps.
- Apply statistical methods (e.g., Chi-Square, ANOVA) to validate improvements and track supplier performance.
- Quantify the impact of quality issues on cost, delivery, and product lifecycle.
- Develop interactive dashboards for quality KPIs, inspection results, and supplier scorecards.
Predictive Analytics & Early Warning Systems:
- Build machine learning models for predictive maintenance, defect prevention, and demand forecasting.
- Trigger early alerts for quality failures, supply chain delays, or part defects based on real-time data.
- Analyze machine usage and environmental factors to predict breakdowns and defect patterns.
- Support FMEA initiatives with data-backed failure analysis and root cause identification.
Cross-Functional Collaboration & Project Leadership:
- Lead analytics efforts aligned with strategic manufacturing goals and data transformation initiatives.
- Collaborate closely with IT, data engineering, supply chain, and procurement teams to ensure infrastructure readiness and analytical impact.
- Guide stakeholders in interpreting insights and driving measurable business outcomes.
Visualization & Self-Service Reporting:
- Develop advanced dashboards and reporting layers using Power BI and Excel for strategic and operational decisions.
- Enable self-service analytics by designing modular and user-friendly reports.
- Provide real-time visibility into KPIs, except reporting, and performance trends.
Educational Requirements: Bachelor's or master's degree in a relevant field (e.g., Data Science, Business Analytics, Information Technology).
Special Certification or Training Required:
- Certified Data Management Professional (CDMP).
- Project Management Professional (PMP).
- Advanced degrees or certifications in data analytics or data science are also valuable.
Required Industry Experience: At least 5-7 years of relevant industry experience, which includes roles in data analysis, data management, or related fields.
Technological Requirements:
- Proficiency with data-related software and tools, such as:
- Data analytics and visualization tools (e.g., Tableau, Power BI, Python, R).
- Data management and warehousing platforms (e.g., SQL, Hadoop, AWS, Azure).
- ELT tools (e.g., Informatica, Talend).
- Data governance and quality tools.
- Familiarity with cloud-based data solutions is often beneficial.
Language Requirements: Excellent command of the English Language.
