GCP Data Engineering Trainer Needed -- 2

I plan to run a practical-heavy Google Cloud Platform course that guides an intermediate audience through the core data-engineering services—BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Storage, and Composer. The group already understands basic GCP navigation; what they need now is the industrial “how it’s really done” perspective: designing pipelines, optimising cost, securing data, and automating workflows at scale. The programme should balance theory with hands-on work. I expect each concept to be introduced with a concise architectural walkthrough and then reinforced by a lab where learners build or tweak a working pipeline on their own accounts. Please weave in best-practice checkpoints, common production pitfalls, and tips on monitoring with Cloud Logging and Cloud Monitoring. Deliverables • A detailed syllabus broken into logical modules (approx. 10–12 sessions) • Slide deck and speaker notes for each module • Step-by-step lab guides and starter code or Terraform snippets • Sample datasets or public data links suitable for the exercises • Post-session quizzes and a capstone project brief • One hour of follow-up Q&A support after the last session Acceptance criteria: labs must execute successfully on a newly created GCP project with default quotas; all materials should be shareable in Google Docs or Markdown; terminology and screenshots must align with the current console UI. If you have real production stories around streaming ingestion, schema evolution, or cost optimisation, those examples will be especially valuable.

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