⚙️ Data Engineer Roadmap
Your guide to building and maintaining robust data pipelines and infrastructure
1
Programming & CS Fundamentals
3-6 months
Core Programming
Master Python for data tasks and develop deep proficiency in SQL for data querying and transformation.
CS Foundations
Understand essential data structures, algorithms, operating systems, and networking concepts.
Development Tools
Gain proficiency in version control with Git and containerization with Docker for reproducible environments.
2
Databases & Data Warehousing
3-6 months
Database Systems
Learn the principles and use cases for relational (PostgreSQL) and NoSQL (MongoDB, DynamoDB) databases.
Data Warehousing
Master concepts of modern cloud data warehouses like Snowflake, BigQuery, and Redshift for analytics.
Data Modeling
Understand techniques for designing effective data schemas, including dimensional modeling (star/snowflake).
3
Big Data Technologies
4-6 months
Batch Processing Frameworks
Gain deep expertise in Apache Spark for large-scale distributed data processing and analytics.
Stream Processing
Learn to build real-time data pipelines using technologies like Apache Kafka, Flink, and Spark Streaming.
Data Lake & Lakehouse
Understand Data Lakes (S3/GCS) and the modern Lakehouse architecture (Delta Lake, Iceberg, Hudi).
4
Data Orchestration & Pipelines
Ongoing Practice
Workflow Orchestration
Master tools like Apache Airflow or Dagster to schedule, monitor, and manage complex data workflows.
ETL/ELT Design
Learn to design, build, and optimize robust and scalable ETL (Extract, Transform, Load) and ELT pipelines.
Data Quality & Testing
Implement frameworks like Great Expectations and dbt tests to ensure data accuracy and reliability.
5
Cloud & DataOps
Ongoing
Cloud Platforms
Gain hands-on experience with the data services of major cloud providers (AWS, GCP, Azure).
Infrastructure as Code (IaC)
Use Terraform to define and manage your data infrastructure programmatically for consistency and scalability.
CI/CD for Data (DataOps)
Apply DevOps principles to data pipelines, creating automated CI/CD workflows for testing and deployment.
6
Governance & The Ecosystem
Mastery
Data Governance & Security
Understand principles of data security, privacy (GDPR, CCPA), access control, and data cataloging tools.
MLOps Support
Learn to build the infrastructure and feature stores required to support Machine Learning engineers and Data Scientists.
Modern Data Architectures
Stay current with emerging architectural patterns like the Data Mesh and understand their implications for the enterprise.