How I became
a data engineer.

Data engineering

Data-platform engineering across 13+ external client engagements, ranging from focused technical contributions to ownership of complete platform builds.

• Lead Microsoft Fabric and Azure Synapse work across architecture, batch and real-time data loading, implementation, and production operations.
• Built CI/CD, monitoring, delta-history patterns, and capacity automation for multi-environment data platforms.

Production ML

Production machine learning and pricing systems connected directly to marketing, growth, and product decisions.

• Delivered a production lead-scoring system that contributed to a measured 38% increase in conversion rate.
• Built customer-lifetime-value models and led dynamic pricing across five products and two markets.

Applied data science

The bridge between academic data science and production work, completed alongside my master’s degree.

• Built automated Databricks pipelines and web scrapers for recurring competitor and market data.
• Developed my master’s thesis on estimating insurance-pricing models from limited data using XGBoost.

First data role

My first professional data role, focused on acquiring market data and turning it into usable analysis.

• Built an automated Python scraper for insurance-market data.
• Created SQL monitoring dashboards and analysed market trends in Python.

Data-science foundation

Advanced study in machine learning, statistics, and big-data analytics, connected to applied work at Getsafe.

• Studied machine learning, statistics, and big-data analytics.
• Completed an industry thesis at Getsafe on hybrid pricing models for limited insurance datasets.

Software foundation

The software-engineering foundation behind the data platforms and production systems I build today.

• Studied software engineering, application architecture, and full-stack development.
• Completed my bachelor’s thesis by building Alya, an open-source smart-mirror project spanning desktop, mobile, cloud, and Raspberry Pi components.