Transition from analytics engineering to AI engineering by leveraging your data modeling expertise and SQL mastery. As an analytics engineer, you already possess critical skills that translate directly to AI work: dimensional modeling maps to feature engineering, dbt transformations parallel ML pipeline architecture, and your experience with data quality testing provides a foundation for AI evaluation frameworks. Your deep understanding of data lineage, schema design, and transformation logic gives you an edge in building reliable AI systems that depend on clean, well-structured data. The patterns you use daily in dbt (modularity, testing, documentation, version control) are exactly what production AI systems require. This path builds on your analytics foundation, teaching you to apply familiar concepts like staging layers and incremental processing to ML feature stores and RAG pipelines. You will learn Python as a complement to SQL, focusing on pandas and the data transformation libraries that feel natural to SQL practitioners. Timeline: 4-6 months.
Skills You'll Build
Skills You'll Build
Skills You'll Build
Skills You'll Build
Skills You'll Build
Skills You'll Build