Build production data platforms on Google Cloud – BigQuery, Dataflow, Pub/Sub, and AI-powered analytics pipelines.
6 months
Live cohort + cloud labs
Beginner to job-ready
Python and advanced SQL - the daily muscle of every data engineer.
Data modeling, warehousing, and modern ELT patterns.
BigQuery deeply: partitioning, clustering, performance, and cost control.
Batch and streaming pipelines with Dataflow (Apache Beam) and Pub/Sub.
Orchestration with Cloud Composer (Airflow) and dbt on BigQuery.
AI on the warehouse: Vertex AI, BigQuery ML, and LLM-powered analytics.
Every skill is practiced through builds – not slides.
→ Python for data engineering
→ Advanced SQL & window functions
→ Linux, Git, and GCP basics
→ Data modeling (Kimball + modern)
→ BigQuery internals & cost control
→ Partitioning, clustering, performance
→ Batch ELT with dbt on BigQuery
→ Streaming with Pub/Sub + Dataflow
→ Schema evolution & data quality
→ Airflow / Cloud Composer DAGs
→ CI/CD for data pipelines
→ Monitoring, alerting, lineage
→ BigQuery ML for in-warehouse ML
→ Vertex AI pipelines & endpoints
→ LLM-powered analytics & RAG over data
→ End-to-end GCP data platform
→ GCP Data Engineer cert prep
→ Interview prep + referrals
Ingest raw data, model it with dbt, and ship trusted marts.
Pub/Sub + Dataflow pipeline processing events into BigQuery in real time.
Airflow DAGs on Cloud Composer running production-grade workflows.
BigQuery ML + LLM interface that lets users query data in natural language.
No. We start with GCP fundamentals and ramp up to production patterns.
Yes – content closely tracks the Professional Data Engineer exam.
Yes. Every phase ships running pipelines, not just slides.
Limited seats. Real mentorship. Real outcomes.
