Data Engineer

Syllabus (1 Month · ~20 sessions · 1 hr each)

Week 1 — Data Engineering Foundations

• Session 1: What is Data Engineering vs Analytics, pipeline overview
• Session 2: Data types, structured vs unstructured, batch vs stream
• Session 3: SQL deep dive — SELECT, filters, aggregations
• Session 4: JOINs, subqueries, views
• Session 5: Practice SQL on sample schemas

Week 2 — Python for ETL

• Session 6: Python refresh for data work
• Session 7: Reading/writing CSV, JSON, basic file ETL
• Session 8: Pandas for cleaning & transformation
• Session 9: Connecting Python to MySQL / SQLite
• Session 10: Simple ETL script (extract → transform → load)

Week 3 — Pipelines & Warehousing Concepts

• Session 11: ETL vs ELT, staging, dimensions & facts (intro)
• Session 12: Data warehouse concepts (star schema overview)
• Session 13: Scheduling ideas (cron / simple scheduler)
• Session 14: Data quality checks & logging basics
• Session 15: Practice — end-to-end mini pipeline

Week 4 — Tools Intro & Project

• Session 16: Intro to cloud data tools (overview only: S3, BigQuery/Redshift concepts)
• Session 17: Intro to orchestration (Airflow concepts — high level)
• Session 18: Project design & best practices
• Session 19: Mini project (CSV/API → clean → load to DB + simple report)
• Session 20: Project review, revision & assessment

Outcome: Understand data pipeline basics, write simple Python ETL, use SQL confidently, complete a mini end-to-end pipeline.