Learning from search-performance data.
Hands-on experience with Python feature preparation and model evaluation, supported by SQL and DuckDB.
I’m a BI Developer with nearly two years of freelance experience turning business questions and scattered data into clear, reliable Power BI dashboards. I work across the reporting process—from data preparation and validation with Excel, SQL, and Power Query to dimensional modeling, DAX measures, KPI definitions, interactive reports, and practical handover documentation.
My portfolio explores reporting and analysis across different teams, customers, and business models.
My work has touched sales and retail, churn, supply chain, e-commerce, customer satisfaction, marketing, HR, and healthcare. Beyond BI, I’ve worked with Python, SQL, and DuckDB on search-performance data during an ML internship; mentored learners in Excel and analytics; and coached aspiring freelancers. I hold a BSc, and continue to develop through independent analytics projects and Excel/VBA automation, exploring ways to make reporting more efficient and useful.
Hands-on experience with Python feature preparation and model evaluation, supported by SQL and DuckDB.
Mentored learners in Excel and analytics, and coached aspiring freelancers on positioning, portfolios, proposals, and client communication.
Independent analytics projects and Excel/VBA automation help me explore practical ways to make reporting more efficient, reliable, and easier to repeat.
These numbers describe the public portfolio and current course log. Open the source collection behind each one.
Public GitHub originals; forks are counted separately.
1212The question, implementation choices, and limitations.
2222Power BI previews, published reports, and source files.
77Current coursework, separate from earned credentials.
Choose a focus to explore the workflow and the public projects behind it.
Data engineering workflow selected.
The questions I look for: What is the grain? Where can a record fail? Can the transformation and business definition be traced?
Files, databases & APIs
Preserve the raw inputs
Check keys, types & quality
Dimensions, facts & grain
A documented semantic layer
I connect a business question to model definitions, DAX measures, report interactions, and the context a reader needs to interpret the result.
Define the business decision
Relationships & definitions
DAX & explicit KPI logic
Filters, pages & drilldowns
Explain the limits & next step
My current focus connects repeatable preparation, feature engineering, leakage-aware evaluation, and outputs that make sense in a real workflow.
Clean, documented datasets
Keep evaluation isolated
Respect what is known when
Train & compare approaches
Inspect errors & usefulness
I’m interested in workflows that connect APIs, queues, storage, and AI components with validation, error handling, and useful operational notes.
An event, schedule, or request
Separate intake from execution
Tools, APIs & guarded AI steps
Persist the useful result
Logs, recovery & handover