Operations Analytics | Analytics Engineering | Telecom Operations
Brazil · SQL · Python · dbt · BigQuery · DuckDB · Business Intelligence
I bring more than 23 years of experience in telecommunications operations, performance monitoring, and service management. My work connects operational knowledge with data analysis, process automation, and the development of reliable business metrics.
My portfolio focuses on translating business questions into SQL analyses, data models, and dashboards, with applications in SLA monitoring, backlog management, revenue analysis, and operational performance.
Professional focus: Analytics Engineering, Operations Analytics, data modeling, and automation.
| Area | Technologies and practices |
|---|---|
| Analytics Engineering | dbt Core, staging/intermediate/marts, dimensional modeling, data tests, and lineage with source() and ref() |
| SQL & analytical databases | BigQuery, PostgreSQL, DuckDB, CTEs, joins, window functions, aggregations, ARRAY, STRUCT, and UNNEST |
| Python & statistics | pandas, NumPy, SciPy, exploratory analysis, visualization, and hypothesis testing |
| Business Intelligence & automation | Power BI, DAX, Power Query, Looker Studio, AWS QuickSight, Excel, and Power Automate |
| Development | Git, GitHub, GitHub Codespaces, VS Code, and Jupyter |
DuckDB · dbt Core · BigQuery · GitHub Actions · Streamlit
End-to-end Analytics Engineering pipeline. A Python/DuckDB generator creates intentionally corrupted data (duplicates, mixed date formats, dirty currency strings, missing values). dbt models in BigQuery clean it through staging, intermediate and marts layers, with multi-currency conversion and an SCD Type 2 customer dimension. Includes 14 data tests, a CI workflow that runs dbt build on every pull request, and a live Streamlit dashboard with business KPIs and a data quality audit page.
DuckDB · dbt Core · Python · Parquet
Operational analytics using 10,000 synthetic telemetry events. The documented implementation includes a staging model and an aggregated data mart for SLA breach analysis by network node, supported by Python visualizations. The project is under development.
dbt Core · BigQuery · SQL · YAML
Layered transformation pipeline over the public TheLook E-Commerce dataset. Includes order-level financial aggregation, gross margin calculations, and the fct_orders and dim_customers models. The repository documents 24 data tests covering uniqueness, nullability, relationships, and accepted values.
Power Automate · Python · Power Query · Excel COM · Windows Task Scheduler
End-to-end automation of operational data updates including XLSB-to-XLSX conversion, Power Query refresh, ETL refresh, audit logging, scheduling and unattended execution. The solution is fully documented, versioned with Git/GitHub and running in production.
dbt Core · BigQuery · SQL · Jinja
Analytics pipeline over a dataset documented at more than 2.9 million trips. Includes staging and intermediate transformations, station and trip models, a monthly station summary, temporal categorization, and separate development and production targets.
PostgreSQL · SQL · Looker Studio
Educational logistics scenario combining analytical views, operational and financial KPIs, and an executive dashboard. Includes 15 documented SQL analyses covering routes, drivers, revenue, margins, rankings, and temporal trends.
BigQuery · SQL · ARRAY · STRUCT · UNNEST
Analysis of nested and repeated session data from the public Google Analytics sample dataset. Queries explore conversion, revenue, acquisition channels, landing pages, devices, and engagement.
DuckDB · Python · pandas · GitHub Codespaces
Reproducible SQL study environment with CSV loading and table registration in DuckDB. Includes reusable SQL scripts and notebooks for joins, aggregations, window functions, rankings, and exploratory analysis.
DuckDB · Advanced SQL · Python · Jupyter Notebook · Data Engineering
A hands-on learning project covering DuckDB architecture, advanced SQL analytics, temporal joins, data integration, and query optimization. The repository features 14 technical chapters with Jupyter notebooks, guided labs, practical exercises, and Operations Analytics use cases. Status: Active development and technical validation.
- BigQuery Advanced Aggregations — Seven documented queries using
APPROX_COUNT_DISTINCT,APPROX_QUANTILES,ARRAY_AGG,STRUCT,LOGICAL_AND, andLOGICAL_OR. - Loja Premium Sales Analytics — Google Sheets, BigQuery SQL views, and Looker Studio, with Brazilian currency string cleaning and type standardization.
- Telecom Revenue Analysis — Educational comparison of ARPU between customer plans using synthetic data, Python, pandas, and Welch's t-test.
- AWS QuickSight Analytics Dashboard — Interactive filters, parameters, KPI monitoring, and regional and customer satisfaction analyses.
- Looker Studio + BigQuery Dashboard — SQL views, calculated fields, and interactive business reporting.
Browse all public repositories
Completed learning tracks
- DataCamp — Associate Data Analyst in SQL
- DataCamp — Data Analyst in Python
- DataCamp — Data Analyst in Databricks
- DataCamp — dbt Fundamentals
Courses & Professional Development
- Power BI — Formação Completa em Power BI · Power BI Specialist
- Udemy — Estatística para Análise de Dados · Álgebra Linear para ML · SQL para Análise de Dados · AWS QuickSight
- LinkedIn Learning — Fundamentos de Estatística
Current learning priorities
- DuckDB + Polars for Operations Analytics, with advanced dbt practice
- Continued development in Python, statistics, and Data Science
Study lab: Databricks Lab — SQL, Python, PySpark, Delta Lake, and Lakehouse studies.
Interested in technical exchanges and collaboration on analytics, automation, and operational data projects.