Roadmap to Become a Data Engineer in 10 Stages

Stage 1 → SQL & Database Fundamentals
Stage 2 → Python for Data Engineering (Pandas, PySpark)
Stage 3 → Data Modelling & ETL/ELT Design (Star Schema, CDC, DWH)
Stage 4 → Big Data Tools (Apache Spark, Kafka, Hive)
Stage 5 → Cloud Platforms (Azure / AWS / GCP)
Stage 6 → Data Orchestration (Airflow, ADF, Prefect, DBT)
Stage 7 → Data Lakes & Warehouses (Delta Lake, Snowflake, BigQuery)
Stage 8 → Monitoring, Testing & Governance (Great Expectations, DataDog)
Stage 9 → Real-Time Pipelines (Kafka, Flink, Kinesis)
Stage 10 → CI/CD & DevOps for Data (GitHub Actions, Terraform, Docker)

👉 You don’t need to learn everything at once.
👉 Build around one stack, skip a few steps if you’re just starting out.
👉 Master fundamentals first, then move to the cloud.

The key is consistency → take it step by step and grow your skill set!