PLC/SPS Programmer and Automation Engineer in Germany, building practical IT/OT portfolio projects based on industrial automation experience.
My background is in Siemens PLC programming, TIA Portal, STEP 7, WinCC/HMI/SCADA, commissioning, troubleshooting, industrial networks, drives, robot interfaces, and production automation systems.
I am expanding my automation background toward IT/OT topics such as industrial network architecture, OPC UA, machine connectivity, production data flow, SCADA/MES interfaces, SQL-based production data modeling, validation, and industrial data analysis.
I am building a practical portfolio that connects automation engineering with IT/OT and industrial data topics.
The main direction is:
PLC / Automation → IT/OT → Machine Data → Production Data → Industrial Analytics
My target direction is an Automation Engineer role with IT/OT focus, or an IT/OT role where my PLC and automation background is a strong technical foundation.
- Siemens PLC programming
- TIA Portal and STEP 7
- WinCC and WinCC Unified
- HMI/SCADA systems
- Commissioning and troubleshooting
- Industrial networks
- Drives and robot interfaces
- PLC-to-HMI and PLC-to-SCADA communication
- Production automation documentation
Conceptual industrial IT/OT network architecture covering factory network segmentation, VLAN/IP planning, firewall rule logic, Industrial DMZ, secure remote access, and communication matrix documentation.
Repository: industrial-it-ot-network-architecture
Conceptual OPC UA documentation project showing machine data flow from PLC systems to SCADA, historians, MES, dashboards, and IT/OT systems using server/client roles, address-space modeling, communication matrices, and secure access concepts.
Repository: opc-ua-machine-data-flow
SQLite-based IT/OT production data model showing machine data, production orders, alarms, cycle records, energy readings, validation queries, and MES-style data flow documentation.
Repository: production-data-model-mes-flow
Python-based project for simulating PLC-style machine operation data, including machine states, fault events, anomaly labels, validation checks, visual outputs, and engineering interpretation.
Repository: simulated-plc-machine-data-anomaly-analysis
Industrial machine learning project focused on predicting steel industry energy consumption using data analysis and machine learning models.
Repository: steel-energy-consumption-prediction
The portfolio follows a practical sequence:
Network → Protocol → Data → Analysis → Industrial AI
This means:
- The network project explains where industrial data moves in an IT/OT environment.
- The OPC UA project explains how selected machine data can be exposed from PLC-level systems.
- The production data project explains how machine and production data can be structured in SQL for MES-style reporting.
- The simulated machine data project shows how PLC-style machine behavior, faults, and anomalies can be generated, validated, visualized, and analyzed.
- The energy prediction project shows the later direction toward industrial data and machine learning.
- Machine connectivity
- Production data flow
- SCADA/MES interfaces
- OPC UA
- SQL-based production data modeling
- Industrial data validation
- Maintenance-oriented reporting
- Industrial analytics
My goal is to move toward IT/OT and industrial data roles while using my PLC and automation experience as a strong technical foundation.