I work at the intersection of data engineering, database systems, ERP analytics, business intelligence, and applied data science.
My focus is simple: turning messy operational data into reliable pipelines, analytical models, and decision-ready systems.
I am especially interested in modern data platforms, including Databricks, Data Lake, Lakehouse architecture, Delta Lake, and scalable analytics workflows.
- Data Engineering & ETL pipelines
- SQL Server, ERP data, and operational databases
- Power BI semantic models and business reporting
- Databricks, Delta Lake, and Lakehouse architecture
- Time-series forecasting and demand prediction
- Python-based automation and analytics
- Data storytelling for business decision-making
I am building my skills around modern data architecture, especially:
- Data Lake and Lakehouse design
- Bronze / Silver / Gold medallion architecture
- Delta Lake concepts
- Parquet-based data pipelines
- Batch and incremental data processing
- ERP → Lakehouse → BI / ML workflows
- Scalable analytics beyond traditional data warehouses
My goal is to connect legacy enterprise systems with modern analytical platforms.
A forecasting-oriented project focused on product-level demand prediction using ERP-based transactional data.
Main scope:
- SQL-based data extraction
- Python data preparation
- Time-series aggregation
- Feature engineering
- Forecasting with statistical and ML-oriented methods
- Business-aware exception handling
A modern data platform prototype inspired by Databricks and Lakehouse architecture.
Main scope:
- Raw ERP-style data ingestion
- Parquet-based storage
- Bronze / Silver / Gold transformation layers
- Delta Lake-ready design
- BI-ready and ML-ready output datasets
A technical project focused on SQL diagnostics and performance improvement.
Main scope:
- Query analysis
- Indexing strategy
- Execution-plan thinking
- Optimization patterns
- SQL Server-focused performance tuning
A business intelligence project focused on operational KPIs and decision support.
Main scope:
- Power BI semantic modeling
- Dynamic KPI comparison
- Period-over-period analysis
- Warehouse / logistics / employee-level metrics
- Dashboard storytelling
A documentation-focused project for understanding and structuring ERP configuration and reporting logic.
Main scope:
- Reverse-engineering ERP workflows
- Mapping business rules
- Documenting configuration hierarchy
- Translating operational logic into analytical logic
| Area | Tools / Concepts |
|---|---|
| Programming | Python, SQL, VBA, Shell scripting |
| Databases | SQL Server, PostgreSQL, Oracle, MySQL, Cassandra |
| Data Engineering | ETL, ELT, Parquet, data pipelines, batch processing |
| Lakehouse | Databricks, Delta Lake, Medallion Architecture |
| BI & Analytics | Power BI, Power Query, DAX, Excel |
| Data Science | Pandas, Scikit-learn, Statsmodels, time-series forecasting |
| DevOps | Git, GitHub, Docker |
| Enterprise Systems | ERP systems, WMS, logistics data, operational reporting |
- Databricks and Lakehouse architecture
- Delta Lake and medallion pipelines
- Snowflake and modern cloud data platforms
- IBM Data Engineering pathway
- Advanced forecasting and anomaly detection
- MLOps and model monitoring
I like building systems that are:
- Practical
- Explainable
- Business-aware
- Technically structured
- Scalable enough to survive real-world messiness
I am not interested in data work that only looks impressive in theory.
I care about pipelines, models, and dashboards that can actually support decisions.
Thanks for visiting my GitHub.