Data Engineer Mastery Roadmap — From Foundations to Cloud King
This comprehensive 6-month journey builds rock-solid foundations first, then powers through cloud specialization on AWS, Azure, and GCP, ending with hands-on corporate capstone projects.
Pre-Cloud Data Engineering Foundations (Months 1–3)
Cloud Data Engineering Specializations (Months 4–6)
Once foundational skills are mastered, learners specialize in one of the three major clouds with a consistent structure, enabling enterprise expertise and portfolio-ready projects.
AWS Data Engineer Mastery
Monthly Focus
- Month 4: Storage & Ingestion with S3, Glue, RDS, DynamoDB
- Month 5: Big Data Processing with EMR, Lambda, Kinesis
- Month 6: Data Warehousing with Redshift, QuickSight dashboards
Capstone Projects
- Streaming Analytics Pipeline (Kinesis + Lambda + Redshift)
- Secure Data Lake with Glue & Lake Formation
- Automated Data Warehouse Management with Redshift
- ML-Ready Feature Store for SageMaker Integration
Azure Data Engineer Mastery
Monthly Focus
- Month 4: Azure Blob, Data Lake Gen 2, Data Factory Pipelines
- Month 5: Azure Databricks, Stream Analytics, Event Hubs
- Month 6: Synapse Analytics, Power BI Reporting, Security with Key Vault & Purview
Capstone Projects
- Advanced Data Lake Platform with Data Factory & Purview
- Real-Time Streaming Pipeline using Event Hubs & Databricks
- Enterprise Analytics Hub with Synapse & Power BI
- Fully Automated ETL Pipeline with ARM & DevOps
GCP Data Engineer Mastery
Monthly Focus
- Month 4: BigQuery data warehouse, Cloud SQL, Dataflow pipelines
- Month 5: Pub/Sub, Dataproc cluster management, Data Studio for dashboards
- Month 6: Composer (Airflow), Data Catalog, security policies & governance
Capstone Projects
- Cloud-Native Data Warehouse with BigQuery & Dataflow
- Real-Time Analytics Pipeline on Pub/Sub & Looker Studio
- End-to-End Data Lake & Metadata Management using Composer & Catalog
- Automated ML Pipeline using BigQuery ML & Vertex AI
Comprehensive Roadmap Table
| Phase | Focus | Key Technologies & Tools | Deliverables |
|---|---|---|---|
| Pre-Cloud | Python, SQL, Hadoop, Hive, Spark | Python, pandas, SQL, HDFS, MapReduce, Hive, Spark | Foundational ETL pipelines & big data projects |
| Cloud Track 1 | AWS Data Engineering | S3, Glue, Lambda, EMR, Kinesis, Redshift | Streaming Analytics, Data Lakes, Warehousing |
| Cloud Track 2 | Azure Data Engineering | Blob, Data Lake, Data Factory, Databricks, Synapse | Real-Time Pipelines, Data Lakes, BI Dashboards |
| Cloud Track 3 | GCP Data Engineering | BigQuery, Dataflow, Pub/Sub, Dataproc, Composer | Cloud Warehouse, Streaming Analytics, Metadata mgmt |
| Capstone Month | Corporate-Grade Portfolio | All platforms and integrations | 4 Major Production-Ready Projects per Cloud Track |
Why This Roadmap Works
- Foundation First: Ensures every student masters core data programming, SQL, and big data before moving to advanced cloud tools.
- Cloud Specialization: Deep dives into each cloud’s unique strengths and ecosystem, enabling enterprise-level skills.
- Hands-On, Project-Driven: Each stage equips learners with mini-projects building to capstone production projects.
- Portfolio-Ready Graduates: Multiple capstone projects per cloud arm graduates with real-world experience to ace job interviews.