Data Analyst | ETL & Data Quality | SQL | Power BI | Business Intelligence
I build reproducible analytics projects that connect business questions with auditable data pipelines, reconciled metrics and decision-oriented dashboards. My portfolio focuses on evidence you can inspect: source code, tests, CI runs, data contracts, documented limitations and visible results.
I built a reproducible ETL and data-quality workflow to evaluate restaurant concepts and price positioning in Miami without publishing the private educational source.
- Stack: Python, pandas, Jupyter, data contracts, Ruff and GitHub Actions.
- Evidence: 24 automated tests, deterministic 750-customer synthetic demo and CI across Python 3.12–3.14.
- Decision: seafood and vegetarian concepts remain exploratory opportunities whose strength changes under conservative coverage thresholds.
- Reproduce it: run
run_demo.batfrom a clean clone.
I turned six historical NBA datasets into an auditable ETL, a canonical SQL Server model and a six-page DirectQuery Power BI report.
- Stack: Python, pandas, SQL Server, Power BI, DirectQuery and GitHub Actions.
- Evidence: 161,111 input rows, 65,642 unique games, 15 Power BI objects, 11 tests and 89.92% coverage.
- Quality boundary: the repository processes 30.6 MB of versioned real inputs; it does not claim a completed 22 GB run.
- Result: ETL, SQL reconciliation and all six report pages are verified through NBA-I4.
I redesigned and hardened an executive Power BI case around revenue, margin, costs, prior-year performance and a correctly scoped USA drill-down.
- Stack: Power BI, DAX, Power Query, SQL and pbi-tools.
- Evidence: 60,398 fact rows, 35 normalized measures and 68 contexts reconciled through independent DAX and SQL paths.
- Result: four redesigned pages, versionable report source, PBIX/PBIT, automated validation and a published evidence pack.
4. Gestión Financiera · Other project
I maintain a local-first Django application for financed sales, loans, installments, collections, backups and reporting. It demonstrates product delivery and provides a privacy-aware bridge into analytics.
- Stack: Python, Django, SQLite/PostgreSQL, Docker and pytest.
- Evidence: 225 tests, 88% combined line-and-branch coverage and a verified Windows portable release.
- Analytics bridge: reconciled internal dashboard and an 11-file, pseudonymized Power BI-ready export.
- Distribution:
v1.1.0includes the portable ZIP, SHA-256 checksum and release audit.
| Capability | Portfolio evidence |
|---|---|
| Python, pandas and ETL | Miami ETL and NBA Analytics |
| Data quality, testing and contracts | Miami ETL and NBA Analytics |
| SQL Server and dimensional modeling | NBA Analytics |
| DAX, KPI design and Power BI | Financial Performance Dashboard |
| Reconciliation and release controls | Financial Dashboard and Gestión Financiera |
| Business and analytical storytelling | Miami, NBA and Financial projects |
- Data: Python, pandas, NumPy, SQL Server, SQLite and PostgreSQL.
- BI: Power BI, DAX, Power Query, DirectQuery and Excel.
- Quality: pytest, Ruff, data contracts, reconciliations and GitHub Actions.
- Delivery: Git, Docker, release manifests, checksums and reproducible demos.
My background includes technical support, hardware and software troubleshooting, sales and cost management. I use that experience to translate technical findings into clear business decisions and practical deliverables.
- Spanish — Native.
- English — C1 Advanced (EF SET 63).
- Decision-oriented analytics and reporting.
- Reproducible ETL and data-quality workflows.
- BI architecture, metric governance and performance.
- Expanding toward cohort analysis, experimentation and forecasting with backtesting.
- Email:
playeropgames@gmail.com
⭐ I value transparent assumptions, reproducible evidence and conclusions that stay within the limits of the data.