Data Analytics and insights Business Partner - Section Head

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.