Complete Roadmap to Data Engineering

Phase 1:
Discovering a Career in Data

1.1 Understand the Basics

Grasp the fundamental concept and significance of data in today’s context.


1.2 Explore Data Career Paths

  • Explore Data Career Paths: Understand diverse roles in technology.
  • Career Expectations and Salaries: Analyze  the job market and set realistic expectations.

1.3 Overcoming Educational Barriers

  • Formal Degrees vs. Practical Skills
  • Rise Above Formal Computer Science Education: Explore strategies to stand out in the job market without formal degrees.
  • Diversity: Individuals with diverse backgrounds often bring valuable perspectives to the industry.

1.4 Connect with Online Resources

  • Engage in relevant Reddit groups.
  • Subscribe to newsletters.
  • Attend local meetups for in-person or virtual networking.

Phase 2:
Setting Up Your Learning Environment

2.1 Initial Learning Setup

  • Browser-Based Python Coding: Utilize online platforms like for coding now.
  • Understanding Python Notebooks: What are Notebooks? Explore Colab Notebooks for advanced python learning.

2.2 Hardware Considerations

  • Minimum and Recommended Hardware Configurations
  • Additional Considerations: Portability, power, battery life, …
  • Hardware Recommendations: Explore high and low-budget laptops, building your own PC, or budget-friendly second-hand options.

2.3 Software Installation

  • Understanding the Data Engineering Software Stack
  • Basic Software Installation: Python, Git and Github, VS Code, Docker
  • Operating Systems Preparation: Set up for –

2.4 Introduction to Technology Stack

Familiarize yourself with key data technologies and understand how they fit into the bigger picture.

Phase 3: Building Strong Foundational Skills


3.1 Fundamental Skills

  • Programming Basics:  Learning Python through online platforms.
  • Database Fundamentals: Explore databases, SQL, and basic data modeling.
  • Ask Why: Learn the reason behind every skill. This is crucial for beginners.


3.2 Learn Interactively

Interactive Python Learning Platforms; bite-sized lessons with real-time exercises.


3.3 Working with AI and LLMs as learning aids

Fast-track your learning and familiarize yourself with working with AI.


3.4 Online Certifications

Certifications: Get certified by Google and Microsoft early to showcase progress and build confidence.


Phase 4:
Advanced Data Engineering, Machine Learning, and AI


4.1 Advanced Programming and Tools

  • Advanced Python: Advance Python skills for more complex programming tasks.
  • Big Data Tools: Explore tools like Apache Hadoop and Apache Spark for handling big data.


4.2 Learn Data Warehousing

  • Data Warehousing Concepts: Understand the basics of data warehousing
  • ETL (Extract, Transform, Load): Learn the principles of ETL data processing.


4.3 Machine Learning & AI

  • Data Science Fundamentals: Learn machine learning to develop basic models to predict data patterns.
  • Advanced LLM Engineering: Prompt engineering techniques and building thin-shell AI enabled applications.

Phase 5:
Hands-On Experience


5.1 Work on Projects

  • Personal Projects: Initiate personal projects.
  • Open Source Contributions: Browse Github and contribute to open-source projects.
  • Real World Applications Ideas: Search for real-world applications.


5.2 Build a Strong Portfolio

  • Showcasing Projects: Showcase projects on GitHub and create a digital portfolio.
  • Enhanced Visualizations: Enhance your portfolio with compelling visualizations.


5.2 Internships and Entry-Level Positions

  • Volunteering: Seek projects by volunteering for.
  • Internships: Look for internships to gain real-world experience.
  • Entry-Level Positions: Apply for entry-level data engineering positions.
  • Freelancing: Look for freelance opportunities on sites like Upworks and Fiverr.


Phase 6:
Specialization and Certification


6.1 Cloud Platforms

Deepen cloud platform knowledge, focusing on one provider (e.g., AWS, Azure, GCP).


6.2 Narrowing Your Focus: 

Explore different avenues of data engineering and Specialize in one of the following areas.

  • ML Engineering and MLOps: Focus on best practices and emerging technologies.
  • Data Streaming: Develop skills in building real-time data pipelines.
  • Enterprise Products: Explore advanced tools like Snowflake, Databricks, Kubernetes, …


6.3 Certifications: 

Pursue certifications from reputable sources to validate skills.

  • AWS Certified Data Engineer – Associate.
  • Microsoft Certified: Azure Data Engineer Associate.
  • Google Cloud Certified – Professional Data Engineer.

Phase 7:
Continuous Learning, Networking, Career Growth


7.1 Stay Updated

  • Follow Industry Trends: Stay informed about the latest trends and technologies.
  • LinkedIn: Reflect your advanced skills in Data engineering, ML, and LLMs.
  • Networking: Attend events on data and AI.
  • Mentorship: Seek or provide mentorship to foster growth.


7.2 Career Growth

  • Professional Service: Seek mid-sized professional services companies to fast-track your career and salary increase.
  • Career Growth: Anticipate career progression to Solution Architects and Product Manager roles for experienced data engineers.