GCP Data Engineering Trainer Needed -- 2
Budget: ₹1,500 – ₹12,500 INR
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.
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.