🔍 Proactive Data Engineer building scalable, secure, and governed data platforms for the AI era.

With a BSc in Computer Science, I am a Data Engineer who specialises in bridging the gap between raw data and production-ready AI. I design multi-tenant SaaS architectures, private AI data pipelines, and robust ETL/ELT workflows.

My core engineering philosophy is simple: AI should be the interface, not the system. I build the deterministic boundaries, semantic layers, and strict data quality tests required to prevent hallucinations and enable safe, next-generation analytics.

• 🏗️ Data Architecture: Medallion Architecture (Bronze/Silver/Gold), Semantic Layers, Data Isolation
• 🔒 AI Governance: Deterministic Boundaries, Private LLMs (Ollama), MCP Servers, Data Sovereignty
• ☁️ Cloud & Infra: Google Cloud Platform (BigQuery, Vertex AI), Hetzner, AWS, Docker, Nginx
• 🛠️ Data Engineering: Python, SQL, dbt Core, Apache Airflow
• 💡 Interests: Ethical & Explainable AI, Data Privacy Law (GDPR), Open Source Software

• 🏗️ Governed Enterprise AI Data Platform: Engineering an end-to-end pipeline using GCP, BigQuery, and dbt Core. Currently transforming complex, simulated SAP ERP data into clean business metrics validated by strict data quality tests.
• 🤖 Enforcing AI Boundaries: Developing a FastAPI backend integrated with Google Vertex AI to route natural language user intent exclusively to governed dbt metrics.
• 🔐 Privacy-First AI: Architecting local LLM pipelines (Ollama) and multi-tenant SaaS platforms on Hetzner Cloud to ensure 100% data residency and GDPR compliance.
• 🛡️ MCP-Governed Semantic Layers: Designing production-minded semantic layers that translate natural language into validated SQL, preventing unauthorised data access.

"Building reliable data foundations so AI can be the interface, not the system."