English | Español
I turn complex operational and billing data into reliable controls, traceable transformations and useful business decisions.
My background combines real-world financial controls with hands-on Azure data engineering projects. In my current role, I am migrating selected ACL controls to Python and Jupyter: extracting authorized Oracle data, reconciling populations with Pandas, applying explainable business rules, and validating both record counts and monetary amounts.
Python · Jupyter · Pandas · Oracle SQL · SQLAlchemy · oracledb · ACL Analytics · Azure Data Factory · ADLS Gen2 · Databricks · PySpark · Power BI · Excel
| Project | What it demonstrates |
|---|---|
| Financial Controls: ACL/Oracle → Python/Jupyter | Professional control pattern evolving from ACL to Python: Oracle extraction, bidirectional Pandas reconciliation, business-rule justifications, line/amount checks and Jupyter reporting. |
| Azure End-to-End Data Pipeline | ADF ingestion, ADLS Gen2, Databricks, PySpark, Spark SQL and Prophet forecasting in one documented flow. |
| Databricks + ADLS + PySpark Lab | Hands-on lakehouse workflow: storage access, DataFrame transformations, Spark SQL and processed-data persistence. |
| Azure Mapping Data Flow | Visual ETL in Azure Data Factory with cleaning, derived fields and aggregations. |
| MyFigure4ever Business Analytics | A real microbusiness translated into landed cost, unit economics, inventory, cohort analysis and auditable Excel controls. |
| Telco Customer Churn ANN | End-to-end classification workflow using Python and an artificial neural network. |
flowchart LR
A[Business rule] --> B[Source data]
B --> C[SQL / ETL transformation]
C --> D[Quality and reconciliation controls]
D --> E[Analysis or curated output]
E --> F[Explainable decision]
- Business first: I clarify what the number means before choosing the tool.
- Control by design: totals, exceptions and traceability are part of the solution, not an afterthought.
- Explainable delivery: I document assumptions, metric definitions and limitations so the result can be defended in an interview or operational review.
- Responsible evidence: public demos use synthetic or rounded data when the production sources contain confidential information.
I am looking for opportunities in Data Analytics and Data Engineering where I can combine SQL, business understanding, data quality and Azure-based processing. This portfolio is available in English and Spanish because I am prepared to discuss the projects in either language.
Transformo datos operativos y de facturación complejos en controles confiables, transformaciones trazables y decisiones útiles para el negocio.
Mi experiencia combina controles financieros reales con proyectos prácticos de ingeniería de datos en Azure. En mi trabajo actual estoy migrando controles seleccionados de ACL a Python y Jupyter: extraigo datos autorizados desde Oracle, concilio universos con Pandas, aplico reglas de negocio explicables y valido tanto cantidades de registros como importes.
- Primero entiendo la regla de negocio y la definición exacta de cada métrica.
- Incorporo conciliaciones, excepciones y trazabilidad desde el diseño.
- Documento supuestos y limitaciones para que cada resultado sea explicable.
- Utilizo datos sintéticos o métricas redondeadas cuando las fuentes reales son confidenciales.
Busco oportunidades en Data Analytics e Ingeniería de Datos donde pueda combinar SQL, conocimiento del negocio, calidad de datos y procesamiento en Azure.