I turn operational data into practical systems, dashboards, and automations that improve accuracy, reduce manual work, and support better business decisions.
My experience sits at the intersection of business intelligence, supply chain, warehouse operations, and process automation. I use Python, SQL, Power BI, and Azure to solve real operational problems—from reconciliation and reporting to internal applications and workflow optimization.
📍 Open to relocation
🌍 Targeting opportunities in Germany, Canada, and Europe
💼 Interested in BI, data analytics, supply-chain analytics, and operations excellence roles
📫 Reach me at jimhans.sk@gmail.com
- Build interactive Power BI dashboards and decision-support reporting
- Automate repetitive operational workflows with Python
- Analyze warehouse, inventory, fulfillment, and e-commerce performance
- Develop SQL-based data models, KPIs, and reporting pipelines
- Improve process reliability through measurement and root-cause analysis
- Create lightweight internal applications for operations teams
- Improved receiving accuracy from 65% to 98%
- Reduced manual reconciliation work by 80%
- Automated warehouse processes using Python
- Built internal applications supporting warehouse operations
- Identified opportunities to reduce Amazon FBA shipping costs
These results reflect practical, operations-focused analytics: not just reporting what happened, but building tools that help teams perform better.
A configurable Python allocation engine for warehouse and fulfillment planning, with synthetic data, validation, utilization metrics, a command-line interface, and automated tests.
Python CSV Optimization Warehouse Analytics Unit Testing
A tested Python and SQL case study for PO reconciliation, receiving accuracy, operational exceptions, vendor performance, dock-to-stock time, and Power BI-ready outputs.
Python SQL Power BI KPI Design Unit Testing
A reproducible forecasting workflow for demand planning, stock-risk analysis, and forecast accuracy measurement.
Python Pandas scikit-learn Time Series Plotly
A tested Python pipeline for FBA picking reports, box planning, expiry tracking, and fulfillment-efficiency metrics using synthetic data.
Python CSV Data Pipelines Amazon FBA Unit Testing
An application workflow that compares a job posting with a master career profile and produces truthful, role-specific resume guidance, cover-letter drafts, interview questions, and application tracking data.
FastAPI OpenAI API PostgreSQL Supabase Azure DOCX/PDF
| Area | Tools |
|---|---|
| Analytics | Power BI, DAX, Excel, KPI design |
| Programming | Python, Pandas, automation |
| Data | SQL, PostgreSQL, data modeling |
| Cloud | Microsoft Azure, Azure Functions |
| Operations | Warehouse analytics, inventory, receiving, fulfillment |
| E-commerce | Amazon Seller Central, Amazon FBA |
- A public portfolio of realistic, documented operations-analytics projects
- Reusable Python tools for warehouse and supply-chain reporting
- An AI-assisted workflow for tailored, truthful job applications
- Stronger cloud deployment and analytics-engineering capabilities
Open to international opportunities where analytics, automation, and operational experience can create measurable business impact.