About
I like turning messy data into something a business can actually act on. What started as a way to explore that outside the classroom has grown into a collection of projects spanning data engineering, BI, analytics, and a bit of AI.
Back in April 2024, during our studies, a friend and I started Nordic Raven Solutions, a small side practice we use to take on real data work and keep learning. Collaborations so far include Paper Check and my University Dormitory (yes, really).
Since then I have graduated with my MSc in Business Intelligence from Aarhus University (Nov. 2025), and kept building this portfolio to show what I can do with those skills beyond coursework.
What I love doing
| Category | Technologies | What It Means |
|---|---|---|
| Data Engineering / BI | SQL, PostgreSQL, ETL/ELT, Azure, Microsoft Fabric, Power BI, Apache Superset, Arelle (XBRL) | My home base: getting data modeled, cleaned, and trustworthy, then into dashboards people actually use to make decisions. |
| Analytics & Modeling | Python, scikit-learn, XGBoost, LightGBM, Classification/Regression, Forecasting | Comfortable pairing domain knowledge with practical models to answer real business questions, not modeling for its own sake. |
| Generative AI / LLMs | LangChain, RAG, ReACT, OpenAI, Gemini, Transformers, NLP | Build LLM/RAG and agentic tools where they add value. I've used them in real projects and at work, as one tool among many. |
| Tooling & Deployment | Docker, Git, FastAPI, Streamlit, Next.js, GCP, Railway | Can take a project from notebook to something deployed and usable: the glue that turns analysis into a working tool. |
Data Engineering / BI
Technologies: SQL, PostgreSQL, ETL/ELT, Azure, Microsoft Fabric, Power BI, Apache Superset, Arelle (XBRL)
What It Means: My home base: getting data modeled, cleaned, and trustworthy, then into dashboards people actually use to make decisions.
Analytics & Modeling
Technologies: Python, scikit-learn, XGBoost, LightGBM, Classification/Regression, Forecasting
What It Means: Comfortable pairing domain knowledge with practical models to answer real business questions, not modeling for its own sake.
Generative AI / LLMs
Technologies: LangChain, RAG, ReACT, OpenAI, Gemini, Transformers, NLP
What It Means: Build LLM/RAG and agentic tools where they add value. I've used them in real projects and at work, as one tool among many.
Tooling & Deployment
Technologies: Docker, Git, FastAPI, Streamlit, Next.js, GCP, Railway
What It Means: Can take a project from notebook to something deployed and usable: the glue that turns analysis into a working tool.
My Latest Projects
Get in Touch
Interested in working together? Let's talk :)
Contact Information
Feel free to reach out about roles, project collaborations, or just to talk data, BI, and analytics.