Student Data Engineer
København, Capital Region, Denmark
Data Engineering:Developing ETL pipelines in Azure Databricks and DLT, with a focus on industry best practices, such as the medallion framework, ACID transformations, dimensional models, and CDC. Used PySpark and SQL for both streaming and batch sources, such as Meta Ads, GA4, and GSC. Orchestration and CI/CD using git and Azure DevOps.ML/Data Science:Developing data science solutions in Azure. Establishing API endpoint to marketing ad services from providers such as Google and Meta. Designing statistical/deep learning ML models for e-commerce BI solutions, including advertising budget allocations, marketing mixed modelling (MMM), share of wallet (SOW) surveys, and customer lifetime value (CLV) analysis.Ad Hoc:Internal reporting on theoretical aspects of ML applications, including:- CLV modeling: P/NBD, BG/NBD, GG/NBD, etc.- MMM modeling: Adstock transformations, Pareto optimal frontsResearch on applying advanced AI techniques, such as LLMs, GNNs, and BNNs to e-commerce.