Skills & Tech Stack
Hard Skills
Data Engineering
| Technology | Level | Main Use |
|---|---|---|
| Apache Airflow | Advanced | Pipeline orchestration, DAGs, retries, SLAs |
| Apache Spark | Advanced | Distributed processing of large volumes |
| DBT | Intermediate | Layered transformations, data tests |
| Hadoop / HDFS | Advanced | Distributed storage, Big Data ecosystem |
| Apache Hive | Advanced | SQL queries on distributed data |
| Apache HBase | Intermediate | Column-oriented NoSQL database |
| Impala | Intermediate | High-performance analytical queries |
| Apache Oozie | Intermediate | Hadoop workflow orchestration |
| Docker / Compose | Intermediate | Service and pipeline containerization |
| GitLab CI/CD | Intermediate | Test automation, build and deploy |
| GitHub Actions | Intermediate | CI/CD for open source projects |
Data Science
| Technique | Application |
|---|---|
| Supervised classification | Decision Tree, Naive Bayes, Random Forest |
| Regression | Simple and multiple linear, Ridge, Lasso |
| Clustering | K-Means, silhouette analysis |
| Fraud detection | ML applied to fiscal risk analysis |
| Reinforcement learning | Fundamentals and practical applications |
| Graph analysis | Complex networks, corporate ties |
| Technique | Tool |
|---|---|
| Univariate and bivariate analysis | Pandas, Seaborn |
| Descriptive statistics | Statsmodels |
| Outlier treatment | IQR, winsorization |
| Correlation and multicollinearity | VIF, heatmaps |
| Model evaluation | Scikit-learn metrics |
Scikit-learn · TensorFlow · Pandas · NumPy
· Matplotlib · Seaborn · Statsmodels · beartype
Databases
| Database | Type | Experience |
|---|---|---|
| PostgreSQL | Relational | Advanced |
| Oracle Database | Relational | Advanced |
| SQLAlchemy | ORM / Abstraction | Intermediate |
| Databricks | Lakehouse | Intermediate |
| Hive | Distributed SQL | Advanced |
| Impala | Distributed SQL | Intermediate |
| HBase | Columnar NoSQL | Intermediate |
Languages & Tools
- Python 3.13 — main development language
- SQL / Impala — queries, DDL, relational modeling
- Shell Script — automation and pipeline integration
- Java 11+ — OOP, systems and layered architecture
- Git / GitHub / GitLab — versioning and collaboration
- uv / Poetry — modern Python dependency management
- Ruff — ultra-fast Python linting and formatting
- Pytest — automated tests with mocks and fixtures
- Docker — containerization
- GitLab CI/CD / GitHub Actions — deploy automation
- Jupyter Notebook / Lab — exploratory analysis and experiments
- VS Code — general development
- Databricks — cloud Big Data projects
- Jira — agile management (Scrum/Kanban)
Soft Skills
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Systemic Thinking
Ability to see beyond the data — connecting business rules, technical architecture and real impact on decisions.
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Technical Communication
Translating complex analyses into accessible language for non-technical stakeholders, with clear and objective documentation.
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Autonomy and Initiative
Track record of end-to-end deliveries without close management — from conception to production deployment.
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Product-Oriented Vision
Focus on delivering real business value, not just functional code. Every pipeline built with measurable impact in mind.
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Adaptability
6 years of international experience in Japan developed resilience and the ability to operate in radically different contexts — cultural, technical and organizational.
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Complex Problem Solving
Experience with high-complexity challenges: fiscal fraud detection, large-volume data pipelines and integration between legacy and modern systems.
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Multidisciplinary Collaboration
Working with tax auditors, business analysts and developers — translating needs across different profiles.
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Attention to Best Practices
Tested, documented and versioned code by default. Nothing delivered without automated tests, lint and quality review.
Academic Background
Education
- 🎓 Systems Analysis and Development — Unicesumar (2025 – 2026)
- 📊 Data Science Specialization — EBAC (graduation 2026)
- 💻 Information Systems — UFRRJ (2013 – 2017, incomplete)
- 📐 Economics — UFRRJ (2012 – 2013, incomplete)
International Experience
6 years in Japan
The experience in Japan was formative — not just linguistically, but in the way of working: discipline, attention to detail, respect for processes and the ability to operate under high pressure with autonomy. Skills I carry directly into production data work.