Data Engineer
Build robust data pipelines and infrastructure to process and manage large-scale data systems.
Key Skills to Learn
Learning Path
Programming Fundamentals
Learn Python or Java for data engineering.
Duration: 4-6 weeks
SQL Mastery
Deep dive into SQL for complex queries and optimization.
Duration: 6-8 weeks
Database Systems
Understand relational and NoSQL databases.
Duration: 6-8 weeks
ETL/Data Pipeline
Build data pipelines and ETL systems.
Duration: 8-10 weeks
Big Data Tools
Work with distributed computing frameworks.
Duration: 8-10 weeks
Cloud Platforms
Learn cloud data platforms and services.
Duration: 6-8 weeks
Data Warehousing
Design and manage data warehouses.
Duration: 6-8 weeks
Real-World Projects
Build production-grade data systems.
Duration: 8-12 weeks
Tools & Technologies
Programming
- • Python
- • Java
- • Scala
Databases
- • PostgreSQL
- • MongoDB
- • Cassandra
- • Redis
Big Data
- • Apache Spark
- • Hadoop
- • Hive
- • Kafka
Workflow Orchestration
- • Apache Airflow
- • Prefect
- • Dagster
Cloud Platforms
- • AWS (S3, RDS, Redshift)
- • GCP (BigQuery, Dataflow)
- • Azure (Data Lake, Synapse)
Containerization
- • Docker
- • Kubernetes
Hands-On Projects
CSV to Database Pipeline
Build a simple ETL pipeline that reads CSV files and loads data into a database.
Real-time Data Ingestion
Create a pipeline that processes streaming data from APIs or message queues.
Data Warehouse Design
Design and build a data warehouse with star schema for analytical queries.
Distributed Data Processing
Process large datasets using Apache Spark or Hadoop.
Cloud Data Lake
Build a scalable data lake on AWS/GCP with proper governance.
Learning Resources
Online Courses
- • Udemy - The Complete Hands-On Introduction to Apache Spark
- • DataCamp Data Engineering Path
- • LinkedIn Learning - Data Engineering Fundamentals
Books
- • Fundamentals of Data Engineering by Joe Reis & Matt Housley
- • Designing Data-Intensive Applications by Martin Kleppmann
- • The Art of SQL by Stephane Faroult