Data
+42% demand
Data Engineer
Build and manage scalable data pipelines to enable analytics and machine learning workloads.
9-18 months
4.8/5 rating
8 Phases
Start Learning Path
+42%
SQL
Python
Apache Spark
Airflow
Kafka
Skills & Technologies
SQL
Python
Apache Spark
Airflow
Kafka
AWS Glue
BigQuery
ETL
Data Warehousing
NoSQL
Data Engineer Roadmap
Phase 1: Programming & SQL Fundamentals
1.5 months
Topics Covered:
- Master SQL (joins, subqueries, CTEs, window functions, indexes)
- Python basics and libraries (pandas, os, datetime, requests)
- Data structures and basic algorithms
- Writing clean, modular, efficient code
Phase 2: Data Warehousing & ETL
2 months
Topics Covered:
- Understanding ETL/ELT pipelines
- Data warehousing concepts (OLAP vs OLTP)
- Star and Snowflake schemas
- Tools: AWS Redshift, Google BigQuery, Snowflake
Hands-on Projects:
- Build a basic ETL pipeline with Python and load into BigQuery
Phase 3: Data Pipeline Orchestration
1.5 months
Topics Covered:
- Apache Airflow fundamentals
- DAG creation, scheduling, error handling
- Task dependencies and retries
- Monitoring and logging workflows
Hands-on Projects:
- Create a daily batch pipeline using Airflow
Phase 4: Big Data & Distributed Systems
2 months
Topics Covered:
- Apache Spark basics (RDDs, DataFrames, SparkSQL)
- Kafka for real-time streaming
- Batch vs Streaming architecture
- Performance optimization & partitioning
Hands-on Projects:
- Process large log files using PySpark and Kafka
Phase 5: Cloud & Serverless Data Engineering
1.5 months
Topics Covered:
- AWS Glue and Lambda for ETL
- Serverless pipeline design
- IAM roles and cloud security basics
- Cost optimization for data jobs
Hands-on Projects:
- Build a serverless data pipeline using AWS Glue + S3 + Lambda
Phase 6: NoSQL & Data Storage Options
1 month
Topics Covered:
- When to use NoSQL vs SQL
- Key-value stores, columnar stores (e.g., Cassandra, DynamoDB)
- Document stores (MongoDB)
- Indexing and querying best practices
Hands-on Projects:
- Build a product catalog pipeline using MongoDB
Phase 7: Data Engineering Best Practices
1 month
Topics Covered:
- Data quality checks and data validation
- Monitoring data pipelines (logging, alerts)
- Data versioning and reproducibility
- Writing testable and maintainable ETL code
Phase 8: Capstone Data Engineering Project
2 months
Topics Covered:
Hands-on Projects:
- Design a full end-to-end data pipeline (ingestion → transformation → warehouse)
- Incorporate batch and streaming data
- Deploy on cloud (AWS/GCP)
- Set up monitoring and alerting
- Document and share project on GitHub
Tools & Resources
SQL
Python
Apache Spark
Apache Airflow
Kafka
AWS Glue
BigQuery
MongoDB
Snowflake
Docker
VS Code