Learning GCP Data Engineering Bootcamp

I want to spend one focused week mastering end-to-end data-engineering pipelines on Google Cloud. The heart of the bootcamp is hands-on practice: together we will build, deploy, monitor and troubleshoot real ETL flows until I can run them solo with confidence. Primary learning goal My priority is data ingestion, extraction, transformation and storage. We will start with the full ingestion toolkit—DataFlow, Pub/Sub and Cloud Storage—then chain that work into the wider GCP ecosystem: • Storage layers: BigQuery, Bigtable, Cloud Spanner, Cloud SQL, Datastore / Firestore • Transformation & orchestration: DataFusion, DataProc, Cloud Composer, Cloud Scheduler, Cloud Functions • Data quality & cataloging: DataPrep, Data Catalog • Visualisation: Looker, Data Studio Preferred teaching style Live, screen-shared sessions where you demo, then watch me repeat the same steps until they run clean. Short theory bursts are fine, but the emphasis must stay practical: Terraform snippets, gcloud commands, monitoring dashboards, rollback drills—whatever it takes for true muscle memory. Timeline Five to seven consecutive days, roughly three to four hours per day, scheduled to suit both of us. I’m flexible on time of day as long as the full week is covered. Deliverables 1. Daily interactive sessions (recorded). 2. Step-by-step lab guides and code/scripts used. 3. A final mini-project: ingest a public dataset, transform it, land it in two storage targets, expose it in Looker, all orchestrated by Cloud Composer. I should be able to rerun this unattended when we finish. 4. Brief post-bootcamp checklist of further practice tasks. How to win the job Please share past work that proves you have built or taught similar GCP pipelines: links to repos, screenshots, short descriptions—whatever best showcases your experience. A concise outline of how you would structure the week is welcome, but I will base my choice mainly on demonstrated, hands-on project history.

Projekt-ID: 40251882