Analytics Engineer & Data Analyst | Revenue Operations (RevOps) · AI & Agentic BI | SQL · Python · dbt · BigQuery · MCP
Analytics Engineer & Data Analyst with 10+ years across consultative ERP sales (Omie — Brazil's leading cloud ERP), business consulting, and analytics. I diagnose where SaaS, Ecommerce, and Fintech companies lose revenue and build the governed AI pipelines to capture it.
I've sat in the revenue meetings. I know what the VP of Sales needs by Monday morning — and I build the analytics to answer it before they ask.
Python SQL dbt ELT Pipelines BigQuery Databricks MCP Servers LangChain Claude/Gemini APIs FastAPI Looker Studio Streamlit React/NextJS
Building governed AI analytics systems where natural language queries return deterministic, dbt-governed insights in 15 seconds — no hallucinations, no metric drift.
BI Architecture · Revenue Analytics · Marketing & Funnel Analytics · SaaS Unit Economics (CAC, LTV, MRR, Churn, ROAS) · Financial Modeling · ELT Architecture
🎓 TripleTen Data Science Resident (700h+) · Six Sigma Green Belt · Final-round candidate — Epic Games 2026
| Category | Project | Stack |
|---|---|---|
| 🏆 AI / Analytics Eng | Full-Funnel AI Analytics Platform | dbt · MCP · BigQuery · XGBoost · Claude/Gemini |
| RevOps / AI | RevOps Lead Engine: AI-Powered B2B Command Center | Python · Streamlit · Plotly · XAI |
| Product Strategy / UXR | Epic Games Store: 2026 Ecosystem Intelligence Audit | Python · Streamlit · Random Forest · NLP |
| GenAI / BI | Conversational BI & Generative Analytics (Music Trends) | Streamlit · LLMs · LangChain |
| Auto / Market | Automotive Market Intelligence & AI Agent | Python · Gemini AI · Plotly |
| Sales / Strategy | Strategic Revenue & Churn Analysis (Telecom) | Python · Stats · Business Logic |
| Gaming / Strategy | Global Gaming Market Strategy | Python · Stats · EDA |
| Stats / Edu | PunkSQL — Mobile SQL Learning Platform | Next.js · SQLite/WASM · Vercel · Google OAuth |
| Infra / AI Chatbot | Portfolio Website + AI Chatbot Infrastructure | React · Gemini · Cloud Run · LangFuse · Docker |
Natural language queries · dbt Semantic Layer · 7 MCP Servers · ML Lead Scoring · $0/month base cost
Business Context: Companies run ads across Google, Meta, and organic channels. Marketing claims leads. Sales says they're low quality. The CEO asks: "Where should we spend next quarter?"
Answering this requires joining data from 5+ platforms, building attribution models, scoring leads, and making it all accessible to non-technical stakeholders. This project builds the production system — at $0/month base cost.
Architecture: Three Heads, One Spine
- AI Layer — 7 MCP servers + Claude Desktop, OpenCode, Gemini CLI, Antigravity. Ask questions in plain English, get production-grade React dashboards in 15 seconds.
- BI Layer — Looker Studio + Streamlit + Claude React artifacts. Same governed metrics across every output.
- ML Layer — XGBoost trained on 93K rows + FastAPI
/scoreendpoint + n8n auto-routing. Hot leads bypass the queue instantly.
Key Numbers:
- 29 dbt models — full Medallion Architecture (Bronze → Silver → Gold)
- 2.2M+ rows of real Olist ecommerce data + synthetic marketing data
- 7 MCP servers — Google Ads, Meta Ads, GA4, HubSpot, Salesforce, BigQuery, dbt Semantic Layer
- 4 attribution models — First-Touch, Last-Touch, Linear, Time-Decay
- 5 warehouses — BigQuery, DuckDB, Snowflake, Databricks, Supabase
- 4 AI clients — Claude Desktop, OpenCode, Gemini CLI, Antigravity
- $0/month base infrastructure cost
The Core Insight — Why Governance Matters: Most AI-to-SQL tools fail because they lack a source of truth. This project solves that with the dbt Semantic Layer (MetricFlow): define "ROAS" once in YAML, and every AI client, dashboard, and ML pipeline consumes the exact same definition — forever. No hallucinations. No metric drift.
Full-cycle autonomous lead generation · Predictive scenario modeling · Post-sales retention analytics
Business Context: Most B2B sales teams burn 60–70% of SDR time on manual prospecting. This project builds a fully autonomous RevOps platform — from ICP-driven lead discovery to AI-powered scoring, predictive revenue modeling, and post-sales retention tracking.
Key Features:
- AI RevOps Copilot: Natural-language chat for pipeline risk analysis and quota pacing
- Revenue Scenario Modeler: 4 levers (Volume, Win Rate, ACV, Cycle Time) → 90-day S-curve revenue projections
- Post-Sales NDR Module: Net Dollar Retention, Account Health scoring, ARR Waterfall charts
- Explainable AI (XAI): Every lead score includes transparent reasoning — no black-box models
- 10 Integrated Modules: Revenue Dashboard, Lead Intelligence, Sales Navigator, Pipeline Analytics, and more
Business Context: A strategic audit transitioning the Epic Games Store from a digital storefront to an "Ecosystem of Intelligence." Random Forest Regression (R²=0.39) and K-Means Clustering decode the "UX Alpha" — proving 60% of player satisfaction is driven by intangible factors beyond price and specs.
Key Findings:
- The "Hardware Wall": High system requirements correlate negatively (-0.133) with user ratings — a critical churn zone
- Behavioral Segmentation: 4 Product Personas mapping the "Premium Friction" risk in high-cost Indie titles
- Final-round candidate for Epic Games Data Analyst role (2026)
What it is: A mobile-first SQL learning platform with a cyberpunk terminal aesthetic. Write real SQL queries that execute in your browser using SQLite compiled to WebAssembly. No backend, no signup required to play.
Key Features:
- 80 SQL challenges across 8 modules — SELECT → CTEs, sequential unlocking
- Real SQLite execution via WASM — queries validated against expected output
- Gamified — 20 levels, XP system, 10 achievements, sound effects
- Google OAuth — persistent progress across sessions
- Bilingual — full EN/PT-BR support
- $0/month infrastructure — fully client-side
Streaming AI chatbot · Google Cloud Run · GitHub Actions CI/CD · LangFuse LLMOps · 4-layer security · $0/month
What it is: The production infrastructure powering this portfolio — not a side project, but the live system you're looking at right now. Built to be a proof of work in itself: a streaming AI chatbot, containerized via multi-stage Docker, deployed on Google Cloud Run through a zero-touch GitHub Actions pipeline, with every conversation traced in LangFuse for cost, latency, and security analysis.
Architecture:
GitHub push → Actions: Docker build → Artifact Registry → Cloud Run deploy
Browser → React 19 + Vite (SSE streaming) → Express proxy → Gemini 2.5 Flash
→ LangFuse (trace every token)
Key Features:
- Streaming AI chatbot — Server-Sent Events deliver token-by-token responses; no waiting for the full reply
- 4-layer security model — client sanitization, IP rate-limiting, canary token injection, intent classification (jailbreak detection)
- LLMOps with LangFuse — every conversation traced with cost ($0.15/1M input), latency, and safety flag
- Zero-touch CI/CD — push to
main, site is live in ~3 minutes via OIDC-authenticated GitHub Actions - $0/month at rest — Cloud Run scales to zero when idle
Open source — replicate it yourself:
Business Context: Stakeholders need quick answers but lack SQL skills. This solves that by integrating an LLM directly into the dashboard — ask "What was the most popular genre in the 80s?" and get an instant data-backed answer. Analysis of 170k+ tracks spanning 100 years.
Business Context: Tool-Calling AI Agent using Gemini 2.5 Flash that writes and executes Python code in real-time to answer complex questions about vehicle depreciation, market saturation, and pricing trends.
Business Context: Comparative revenue analysis of mobile plans to identify user behaviors, inform marketing budget allocation, and maximize ARPU. Statistical hypothesis testing to validate profitability strategies.
Business Context: Identifies success drivers in the console/PC gaming industry by analyzing global historical sales data. Builds a predictive framework to support Go-To-Market strategies and mitigate launch risks.
📓 Available as a deep-dive technical notebook — extensive EDA, hypothesis testing, and strategic analysis. Portuguese only.
Analytics Engineer & Data Analyst | Revenue Operations (RevOps) · AI & Agentic BI | SQL · Python · dbt · BigQuery · MCP
Analytics Engineer & Data Analyst com mais de 10 anos entre vendas consultivas de ERP (Omie — maior ERP cloud do Brasil), consultoria de negócios e analytics. Eu identifico onde empresas de SaaS, E-commerce e Fintech perdem receita e construo os pipelines de IA governados para capturá-la.
Já estive nas reuniões de receita. Sei o que o VP de Vendas precisa na segunda de manhã — e construo a analytics para responder antes de ser perguntado.
Python SQL dbt Pipelines ELT BigQuery Databricks MCP Servers LangChain Claude/Gemini APIs FastAPI Looker Studio Streamlit React/NextJS
Construindo sistemas de analytics com IA governada — onde consultas em linguagem natural retornam insights determinísticos, governados pelo dbt, em 15 segundos. Sem alucinações. Sem metric drift.
Arquitetura de BI · Revenue Analytics · Analytics de Marketing & Funil · Unit Economics SaaS (CAC, LTV, MRR, Churn, ROAS) · Modelagem Financeira · Arquitetura ELT
🎓 Residente em Data Science na TripleTen (700h+) · Green Belt Lean Six Sigma · Finalista — Epic Games 2026
| Categoria | Projeto | Stack |
|---|---|---|
| 🏆 IA / Analytics Eng | Full-Funnel AI Analytics Platform | dbt · MCP · BigQuery · XGBoost · Claude/Gemini |
| RevOps / IA | RevOps Lead Engine: Central de Comando B2B | Python · Streamlit · Plotly · XAI |
| Estratégia de Produto / UXR | Epic Games Store: Auditoria de Inteligência (2026) | Python · Streamlit · Random Forest · NLP |
| GenAI / BI | BI Conversacional & Analytics Generativo | Streamlit · LLMs · LangChain |
| Auto / IA | Inteligência de Mercado Automotivo & Agente IA | Python · Gemini AI · Plotly |
| Vendas / Estratégia | Otimização de Receita & Churn (Telecom) | Python · Stats · Lógica de Negócios |
| Gaming / Estratégia | Estratégia Global de Mercado de Games | Python · Stats · EDA |
| Edu / SQL | PunkSQL — Plataforma de Aprendizado de SQL | Next.js · SQLite/WASM · Vercel · Google OAuth |
| Infra / AI Chatbot | Infraestrutura Portfolio Website + AI Chatbot | React · Gemini · Cloud Run · LangFuse · Docker |
Contexto de Negócio: Empresas rodam anúncios no Google, Meta e canais orgânicos. Marketing reivindica os leads. Vendas diz que a qualidade é baixa. O CEO pergunta: "Onde devemos investir no próximo trimestre?"
Responder isso exige unir dados de 5+ plataformas, construir modelos de atribuição, pontuar leads e tornar tudo acessível para stakeholders não-técnicos. Este projeto constrói o sistema de produção — com custo base de $0/mês.
Números-chave:
- 29 modelos dbt — Arquitetura Medallion completa (Bronze → Silver → Gold)
- 2,2M+ linhas de dados reais Olist + dados sintéticos de marketing
- 7 MCP Servers — Google Ads, Meta Ads, GA4, HubSpot, Salesforce, BigQuery, dbt Semantic Layer
- 4 modelos de atribuição — First-Touch, Last-Touch, Linear, Time-Decay
- 5 warehouses — BigQuery, DuckDB, Snowflake, Databricks, Supabase
- $0/mês de custo base de infraestrutura
Contexto: Plataforma RevOps totalmente autônoma — da descoberta de leads por ICP ao scoring com IA, modelagem preditiva de receita e rastreamento de retenção pós-venda. IA Explicável (XAI) em cada score.
Descobertas principais: Hardware Wall (-0.133 correlação), 4 Personas de Produto, 60% da satisfação do jogador impulsionada por fatores intangíveis. Finalista para a vaga de Data Analyst na Epic Games (2026).
Plataforma mobile-first para aprender SQL com estética cyberpunk. 80 desafios, 8 módulos (SELECT → CTEs), execução real de SQL no browser via SQLite/WASM, gamificação completa, Google OAuth, bilíngue EN/PT-BR.
AI chatbot com streaming · Google Cloud Run · CI/CD GitHub Actions · LLMOps com LangFuse · segurança 4 camadas · $0/mês
O que é: A infraestrutura de produção que roda este portfólio — não um projeto paralelo, mas o sistema ao vivo que você está vendo agora. Construído para ser prova de trabalho em si mesmo: chatbot de IA com streaming, containerizado via Docker multi-stage, implantado no Google Cloud Run via pipeline GitHub Actions zero-touch, com cada conversa rastreada no LangFuse para custo, latência e segurança.
Destaques:
- Chatbot com streaming — Server-Sent Events entregam respostas token a token, sem espera
- Segurança em 4 camadas — sanitização no cliente, rate-limit por IP, canary token, classificação de intenção (detecção de jailbreak)
- LLMOps com LangFuse — cada conversa rastreada com custo estimado, latência e flag de segurança
- CI/CD zero-touch — push para
main, site no ar em ~3 minutos via GitHub Actions com OIDC - $0/mês em repouso — Cloud Run escala para zero quando ocioso
Open source — replique você mesmo:
Dashboard interativo com Consultor IA — gestores perguntam em português e recebem respostas baseadas em 100 anos de dados musicais (170k+ tracks).
Agente de IA (Gemini 2.5 Flash) com Tool-Calling que escreve e executa código Python em tempo real para responder perguntas sobre depreciação, saturação de mercado e tendências de preço.
Análise comparativa de planos móveis para maximizar ARPU e identificar padrões de comportamento. Testes de hipóteses para validar estratégias de precificação e retenção.
Contexto de Negócio: Identifica drivers de sucesso na indústria de games analisando dados históricos globais de vendas. Framework preditivo para suportar estratégias Go-To-Market e mitigar riscos de lançamento.
📓 Disponível como notebook técnico — EDA extensivo, testes de hipóteses e análise estratégica. Somente em português.
From: 24 October 2025 - To: 19 April 2026
Total Time: 324 hrs 31 mins
Python 245 hrs 35 mins ██████████████████▓░░░░░░ 74.12 %
Markdown 38 hrs 28 mins ███░░░░░░░░░░░░░░░░░░░░░░ 11.61 %
HTML 10 hrs 43 mins ▓░░░░░░░░░░░░░░░░░░░░░░░░ 03.24 %
TypeScript 9 hrs 29 mins ▓░░░░░░░░░░░░░░░░░░░░░░░░ 02.86 %
Other 6 hrs 48 mins ▓░░░░░░░░░░░░░░░░░░░░░░░░ 02.06 %© 2026 Eduardo Cornelsen — Analytics Engineer & Data Analyst
Diagnosing revenue leakage. Building governed AI pipelines to capture it.