Transition from Looker development to AI engineering by leveraging your deep understanding of semantic data modeling and the Google Cloud ecosystem. As a Looker developer, you already think in abstractions. LookML models define how data should be interpreted, not just queried. This mental model translates directly to AI engineering, where semantic layers, knowledge graphs, and RAG architectures require the same structured approach to making data meaningful. Your experience with dimensional modeling, explores, and derived tables gives you intuition for how to structure information for AI consumption. The SQL expertise you have built is foundational for data pipelines that feed AI systems, while your familiarity with Git-based LookML projects means you already understand version-controlled, collaborative development workflows. Perhaps most importantly, your existing Google Cloud experience positions you perfectly for GCP's AI services. Vertex AI, Gemini APIs, and BigQuery ML integrate naturally with skills you already have. This path focuses on expanding your Python capabilities, understanding how LLMs process and generate text, building RAG systems that mirror the semantic layer concepts you know from Looker, and deploying AI applications on the Google Cloud infrastructure you are already comfortable with. Timeline: 6-8 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
Skills You'll Build