Artemisia Weyl

Data & AI Architect · Data Engineer · Programming Professor · Physicist at heart

data platforms · AI architecture · MLOps · data reliability · cloud · scientific thinking

🌌 Transformando dados brutos em sistemas confiáveis, modelos úteis e conhecimento compartilhado.

🔭 Turning raw data into reliable systems, useful models, and knowledge worth sharing.

flowchart LR
    PHYSICS(("🔭 Physics<br/>research"))
    CORE["🛰️ Data<br/>& AI Architecture"]

    subgraph ORBIT["✦ CURRENT ORBIT ✦"]
        direction TB
        DATA["Data platforms"]
        AI["Responsible AI"]
        GOV["Governance"]
        TEACH["Teaching"]
    end

    IMPACT(("🌍 Systems<br/>that survive<br/>production"))

    PHYSICS -->|hypotheses & evidence| CORE
    CORE --> DATA
    CORE --> AI
    CORE --> GOV
    CORE --> TEACH
    DATA --> IMPACT
    AI --> IMPACT
    GOV --> IMPACT
    TEACH --> IMPACT

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    class CORE core
    class DATA,AI,GOV,TEACH orbit
    class IMPACT impact
    style ORBIT fill:#0b1020,stroke:#c4a7e7,color:#f8f8f2,stroke-width:1px

Sou Arquiteta de IA e Dados e Engenheira de Dados, com 17 anos de trajetória em STEM e 9 anos trabalhando diretamente com dados.

Comecei na Física, pesquisando ciência dos materiais e temas ligados à computação quântica. Foi ali que aprendi a formular hipóteses, testar limites e desconfiar de qualquer explicação que dependa de magia, mesmo quando ela vem acompanhada de um diagrama bonito.

Hoje trabalho com arquitetura de dados e IA, plataformas multi-cloud, machine learning, governança, DataOps e MLOps.

Gosto de problemas grandes, bagunçados e cheios de dependências escondidas. Meu trabalho costuma ser descobrir onde está o gargalo, separar complexidade real de filler e transformar isso em uma arquitetura que alguém consiga operar depois.

Para mim, arquitetura boa é a que continua fazendo sentido quando o slide acaba, o dado chega atrasado, uma API cai e alguém precisa entender o que aconteceu às três da manhã.

Também sou professora de programação e dados. Lecionei na Byju's Future School, produzi conteúdos de Python, SQL e Machine Learning na Coderhouse Brasil e atuo na PUCRS com programação e People Analytics.

Ensinar faz parte da engenharia. Se apenas uma pessoa entende o sistema, temos um jutsu secreto de clã e um ponto único de falha.

Já trabalhei com mercado financeiro, educação pública, operações humanitárias, consultoria e produtos digitais. Atualmente, concentro meu trabalho em arquiteturas responsáveis de IA, plataformas resilientes, observabilidade e governança.

Gosto de construir coisas que funcionem em produção, explicar assuntos difíceis sem ritual de invocação e evitar que uma solução simples vire um arco de 200 episódios.

I am a Data & AI Architect and Data Engineer with 17 years across STEM and 9 years working directly with data.

I started in Physics, researching materials science and topics related to quantum computing. That is where I learned to frame hypotheses, test limits, and question any explanation that depends on magic, even when it comes with a polished architecture diagram.

Today, I work with Data and AI architecture, multi-cloud platforms, machine learning, governance, DataOps, and MLOps.

I enjoy large, messy problems with hidden dependencies. My job is usually to find the real bottleneck, separate actual complexity from filler, and turn the result into an architecture that people can operate after deployment.

A good architecture should still make sense when the slides are gone, data arrives late, an API fails, and someone needs to understand what happened at 3 a.m.

I am also a programming and data professor. I taught at Byju's Future School, created Python, SQL, and Machine Learning content for Coderhouse Brazil, and teach programming and People Analytics at PUCRS.

Teaching is part of engineering. When only one person understands a system, you have a forbidden clan technique and a single point of failure.

I have worked across financial systems, public education, humanitarian operations, consulting, and digital products. My current work focuses on responsible AI architectures, resilient data platforms, observability, and governance.

I like building systems that survive production, explaining difficult topics without a summoning ritual, and preventing simple solutions from becoming 200-episode story arcs.

flowchart TB
    AW(("✦ Artemisia Weyl<br/>Data & AI"))

    subgraph RESEARCH["🔭 RESEARCH ORBIT"]
        IFPA["IFPA<br/>Laboratory Technician<br/>2009–2011"]
        UFPA["UFPA<br/>Computational Physics Research<br/>2012–2017"]
    end

    subgraph DATA["🛰️ DATA & ENGINEERING ORBIT"]
        MIHOYO["miHoYo<br/>CRM & Data Analyst<br/>2020–2021"]
        TENCENT["Tencent<br/>Sr BI Analyst<br/>2021–2022"]
        TEIA["TEIA<br/>Head of Data<br/>2022–2023"]
        IBM["IBM<br/>Sr Data Platform Engineer<br/>2022–2024"]
        SANTANDER["Santander<br/>Data Engineer Spec<br/>2024–2026"]
    end

    subgraph TEACHING["🌠 TEACHING ORBIT"]
        BYJUS["BYJU'S<br/>Programming Professor<br/>2021–2022"]
        CODER["Coderhouse<br/>Lead Instructor<br/>2022–2025"]
        PUCRS["PUCRS<br/>Adjunct Professor<br/>2025–present"]
    end

    subgraph IMPACT["🌍 SOCIAL IMPACT ORBIT"]
        REDCROSS1["Brazilian Red Cross<br/>Research & Field Data<br/>2018–2019"]
        AUTISM["Autismo Tech<br/>Consultant<br/>2024–2025"]
        REDCROSS2["Brazilian Red Cross<br/>State Coordinator<br/>2025"]
        BD["Base dos Dados<br/>Data Project Leader<br/>2025–present"]
    end

    RESEARCH --> AW
    AW --> DATA
    AW --> TEACHING
    AW --> IMPACT

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    classDef research fill:#2b2038,stroke:#f6c177,color:#ffffff
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    classDef teaching fill:#2d1d42,stroke:#c4a7e7,color:#ffffff
    classDef impact fill:#15352f,stroke:#6ee7b7,color:#ffffff
    class AW core
    class IFPA,UFPA research
    class MIHOYO,TENCENT,TEIA,IBM,SANTANDER data
    class BYJUS,CODER,PUCRS teaching
    class REDCROSS1,AUTISM,REDCROSS2,BD impact
    style RESEARCH fill:#0b1020,stroke:#f6c177,color:#ffffff
    style DATA fill:#0b1020,stroke:#67e8f9,color:#ffffff
    style TEACHING fill:#0b1020,stroke:#c4a7e7,color:#ffffff
    style IMPACT fill:#0b1020,stroke:#6ee7b7,color:#ffffff

Electronics Technician
        ↓
Physics: bachelor's + teaching degree
        ↓
Applied Quantum Theory & Computational Modeling: master's degree
        ↓
Complex Systems: doctorate
        ↓
Computer Engineering + CS50
        ↓
Data platforms, governance, cloud, MLOps, and AI architecture

• Doctorate in Complex Systems · USP
• MSc in Physics · Applied Quantum Theory for Materials and Computational Modeling · UFPA
• Computer Engineering · UFPA
• BSc and Teaching Degree in Physics · UFPA
• CS50: Computer Science · Harvard University
• Electronics Technician · Instituto Federal do Pará

LGPD · Zero Trust · DAMA-DMBOK · COBIT · data quality · lineage · observability · regulatory compliance · technical mentorship

research roots       -> physics, materials science, quantum computing materials
architecture focus   -> scalable data platforms, governance, cloud, responsible AI
dataops mindset      -> quality, lineage, CI/CD, observability, reproducibility
mlops practice       -> experiment tracking, model versioning, serving, drift monitoring
leadership orbit     -> strategy, mentorship, multidisciplinary teams, data culture
teaching practice    -> programming, Python, SQL, ML, People Analytics
languages spoken     -> Portuguese, English, Spanish, Japanese
favorite problems    -> trustworthy data, useful AI, robust workflows