I work with data analysis, automation, data pipelines, and business intelligence, building practical solutions with Python, Excel, Power BI, and structured data workflows.
My projects focus on turning operational and commercial data into reliable, auditable, and reusable analytical solutions.
- Python for data processing, automation, validation, and analytics
- Pandas for transformation and analytical pipelines
- Excel / OpenPyXL for automated reports and structured outputs
- Power BI for business intelligence and data visualization
- Pytest for automated testing and regression protection
- Git & GitHub for version control and project organization
- GitHub Actions for continuous integration and automated validation
- Data quality, reconciliation, classification, and deterministic processing
A deterministic product master-data pipeline integrating multiple synthetic sources into a consolidated catalog.
Highlights:
- Multi-source data integration
- Golden record generation
- Attribute-level data lineage
- Conflict resolution
- Data-quality validation
- Quarantine workflow
- Excel, JSON, and JSONL outputs
- Automated tests and CI
A sales and promotional analytics pipeline built with synthetic commercial data.
Highlights:
- Campaign and product performance analysis
- Revenue, quantity, margin, and discount metrics
- Temporal comparison windows
- Channel analysis
- Inventory availability and stockout analysis
- Promotional ABC classification
- Excel and structured analytical outputs
- Cross-platform automated testing
A configurable rule engine for product classification with explainable decisions and data-quality controls.
Highlights:
- Declarative business rules
- Deterministic rule priorities
- Conflict detection
- Explainable classifications
- Data-quality scoring
- Review-required workflows
- Synthetic data
- Automated tests and CI
An analytical project focused on production flows, operational exceptions, and process performance.
Highlights:
- Production order and stage analysis
- Work-in-progress monitoring
- Cycle-time metrics
- Operational exception detection
- Bottleneck identification
- Excel and JSON reporting
- Automated validation
A deterministic inventory consolidation and historical comparison pipeline.
Highlights:
- Inventory snapshot consolidation
- Temporal balance comparison
- Deterministic and idempotent processing
- Automated Excel reporting
- Synthetic datasets
- Automated tests
- Continuous integration
Python · Pandas · OpenPyXL · Pytest · Power BI · Excel · Git · GitHub Actions
Across my projects, I prioritize:
- Reproducible and deterministic processing
- Explicit validation and fail-safe behavior
- Automated regression testing
- Data-quality controls
- Traceability and auditability
- Clear separation between source data, processing logic, and outputs
- Synthetic data for public portfolio projects
All repositories prefixed with portfolio- were developed specifically as public portfolio projects using synthetic data and independent implementations.
They demonstrate patterns and technical approaches without exposing proprietary datasets, internal systems, or confidential business information.