Simon Madsen
I am a Data & AI Consultant with 6+ years of experience helping enterprises build data platforms that power AI, advanced analytics, and smarter decision-making.
I bridge business strategy and technical execution — from Lakehouse architecture and ELT pipelines to AI-ready data models and self-service analytics. I have worked with both executives and engineering teams across many industries including energy, finance, retail, life science and infrastructure.
Technical Skills
Data Platform & Engineering
Microsoft Fabric · Databricks · PySpark · dbt · Delta Lake · Medallion Architecture · Dimensional Modelling
AI & MLOps
LLMs · Agentic Workflows · RAG · MLflow · Prompt Engineering · Vector Databases · Model Deployment
Semantics & Analytics
Power BI · DAX · Power Query · Semantic Models · Row-Level Security · MCP · Context Layer
DevOps & Governance
Azure DevOps · Git · CI/CD Pipelines · Data Governance · Python · SQL · APIs
Education
Achievements
Venture Cup Denmark 2023
Winner - "Best University Startup" for Anemo Robotics. Raised 1.5M DKK in first year.
DTU X-Tech Winner
Winner of DTU's entrepreneurship competition. Successfully transitioned from research to market-ready product.
Certifications
Professional Experience
- Copenhagen Infrastructure Partners: Leading Fabric migration and Medallion architecture implementation with PySpark, DevOps CI/CD pipelines, and release management
- Tivoli: Architected and delivered Fabric Lakehouse with end-to-end ELT pipelines and semantic modeling
- Matas, By & Havn, Novo Life Fonden: Delivered customer care platform, data warehouse solutions, and financial reporting systems
- Designed and implemented data governance frameworks and CI/CD release pipelines across enterprise data platforms
- Full-stack data platform ownership for Europe's 3rd largest fitness chain (2,000+ gyms, 2.5M+ members)
- Designed and maintained Azure data warehouse with ETL pipelines integrating multiple operational source systems
- Delivered 30+ Power BI reports serving 250+ unique monthly users across the organization
- Owned semantic layer design, DAX measures, Row-Level Security, and commercial KPI frameworks
Built end-to-end ML pipelines and computer vision models for autonomous marine biodiversity monitoring — from data ingestion and model training to Azure-based CI/CD deployment
Applied custom-trained LLMs for automated sales-lead classification across hundreds of news outlets
Developed Python scripts for automated stress-testing to validate software releases and hardware integrations