Materials Engineer with hands-on manufacturing and quality experience, transitioning into Data Analytics. I use Python, SQL, PostgreSQL, Power BI and Streamlit to turn production and quality data into practical insights.
My advantage is the combination of engineering domain knowledge, shop-floor experience and data skills — from process capability and SPC to dashboards, defect analysis and continuous improvement.
Lead paintshop operations across production, quality and process performance. Work with powder coating, KTL, robotics, conveyor systems, production planning and continuous improvement, using process data to support operational decisions.
Direct exposure to manufacturing KPIs, process variation, quality data and operational problem solving — providing the domain context behind my move into Manufacturing and Data Analytics.
Analyzed process performance and defects, supported root-cause investigations and applied quality tools to improve manufacturing processes. Built the bridge between engineering knowledge, shop-floor data and practical problem solving.
Developed practical experience translating manufacturing data and quality measurements into actionable process improvements.
Performed non-destructive testing and materials characterization in a laboratory environment, working with measurement data, ultrasonic testing and analytical techniques.
Built a strong measurement and analytical foundation that supports later work with structured quality and production data.
Worked on research and development in materials and non-destructive testing, including automated ultrasound testing, scanning ultrasonic microscopy and testing-system development.
Practical technical training completed in 2025.
Built an end-to-end SPC workflow for manufacturing quality monitoring, using a synthetic powder-coating dataset to demonstrate how production measurements can be transformed into statistical quality insights.
The project connects shop-floor quality measurements with statistical analysis to identify process shifts, assess stability and support data-driven improvement.
Built an end-to-end manufacturing analytics project covering production volume, defect rates, Pareto analysis and downtime, combining data preparation with interactive reporting.
The workflow turns production data into operational KPIs and visual insights that can support quality monitoring, problem prioritization and production improvement.
A collection of early Python projects that demonstrate programming fundamentals through small interactive applications, games and a GUI calculator.
I am interested in Data Analytics, Manufacturing Analytics, Quality Analytics and Process Engineering roles where engineering knowledge and data skills can be combined.
I am particularly interested in data-driven manufacturing, quality improvement, renewable energy, sustainable technology and practical automation.
Open to Data Analyst, Manufacturing Data Analyst, Quality Analyst and Process Engineer opportunities.