Data Engineering Career Path

The Complete Data Engineering Roadmap 2025

A structured, step-by-step learning path from Python fundamentals to production-grade data systems. Follow the phases in order or jump to where you are.

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📅 8 Phases·⏱ 6–9 months·🆓 100% Free Content·💼 Job-Ready Outcome
Learning Path

8 Phases to become a Data Engineer

Each phase builds on the last. Complete all 8 to be ready for senior DE roles at top companies.

01
🐍

Python Fundamentals

⏱ 4–6 weeks

Goal: Write clean Python scripts and work with data files

✓ Variables & Data Types✓ Functions & OOP✓ File I/O & Exceptions✓ Pandas & NumPy Basics✓ List Comprehensions
Start this phase →
02
💾

SQL & Databases

⏱ 3–4 weeks

Goal: Query, transform and analyse data from any relational database

✓ SELECT, WHERE, GROUP BY✓ JOINs — INNER, LEFT, RIGHT✓ Window Functions & CTEs✓ Indexes & Query Optimization✓ PostgreSQL / MySQL Basics
Start this phase →
03
⚡

Apache Spark & PySpark

⏱ 6–8 weeks

Goal: Process massive datasets with distributed Spark jobs

✓ Spark Architecture & RDDs✓ DataFrames & Spark SQL✓ Aggregations, Joins & UDFs✓ Spark Streaming Basics✓ Performance Tuning
Start this phase →
04
🔄

Data Pipeline & ETL

⏱ 4–5 weeks

Goal: Design, build and schedule production ETL pipelines

✓ ETL Design Patterns✓ Apache Airflow & DAGs✓ Data Quality & Testing✓ Incremental Loads✓ Pipeline Monitoring
Start this phase →
05
☁️

Cloud & Data Lake

⏱ 4–6 weeks

Goal: Store and serve petabyte-scale data on cloud platforms

✓ AWS S3, Glue & Athena✓ Delta Lake & Apache Iceberg✓ Data Lakehouse Architecture✓ Partitioning & File Formats✓ Cost Optimisation
Start this phase →
06
📡

Real-Time Streaming

⏱ 3–4 weeks

Goal: Build real-time pipelines processing millions of events/sec

✓ Apache Kafka Fundamentals✓ Spark Structured Streaming✓ Event-Driven Architecture✓ Exactly-Once Semantics✓ Flink Basics
Start this phase →
07
🤖

ML & AI for Data Engineers

⏱ 4–5 weeks

Goal: Integrate ML models into production data pipelines

✓ Feature Engineering at Scale✓ MLlib & SparkML✓ MLOps & Model Serving✓ LLMs & RAG Pipelines✓ Vector Databases
Start this phase →
08
🏗️

System Design & Production

⏱ 3–4 weeks

Goal: Design and defend end-to-end data systems in interviews

✓ Data Architecture Patterns✓ Lakehouse vs Warehouse✓ Interview System Design✓ Monitoring & Alerting✓ Cost & Performance KPIs
Start this phase →
Tools You'll Master
Python
PySpark
SQL
Airflow
Kafka
AWS
dbt
Delta Lake
Spark SQL
Iceberg
Redshift
Snowflake

Ready to start your journey?

Join 10,000+ learners building careers in Data Engineering. All content is free, structured, and production-focused.

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