How to Become Data Engineer in 2026 (Zero To Senior)
Built from 17 years of real data engineering experience
Hey friends - Happy Tuesday!
Last year, I shared a roadmap on how to become a data engineer. I honestly did not expect the impact it would have. Many of you followed it step by step, and some of you even told me you landed your first job.
So I went back and reviewed the entire roadmap. I updated it based on today’s market and extended it.
This time, it does not stop at getting hired. It shows what to learn after you join a company, from Junior to Senior, and eventually to Data Architect.
And Learning data engineering today can be very confusing! There are too many tools, too many skills.
So to clear the confusion:
I explained the full roadmap step by step in this video → LINK
And I shared the Notion template with all phases, resources, and a clear step by step guide → LINK
#1 The Hiring Journey
This journey is about building the right skills to get you ready for your first data engineering role.
Phase 0 - Absolute Beginner 🧭
This phase is about understanding what data engineering really is and deciding whether this career is right for you.
🎯 Mindset: Is data engineering the right career for me?
Understand the Role: You clearly understand the job before investing time and effort.
Make Decision: Decide if data engineering is the right long-term career for you.
Remember: Be honest with yourself. This phase saves you years later.
Ignore hype and salary videos
Ask yourself: “Would I enjoy this work every week?”
Phase 1 - The Foundations 🌱
This phase is about getting you ready by focusing on core skills one by one, so working with data and code starts to feel natural before you even think about data engineering.
🎯 Mindset: I am building my base!
SQL: You can work with data using SQL
Python: Write scripts to process, clean, and transform data.
GitHub: Track changes and manage code professionally.
Remember: Practice more than you consume content
Learn one skill at a time
Focus on understanding, not memorizing
Use AI only to explain concepts, not to write code for you
Avoid tools and platforms for now
Phase 2 - Data Engineering Core ⚙️
In this phase, you start going deeper into data engineering. You’ll learn the core concepts and tools, and more importantly, you’ll start thinking like a data engineer, not just writing code.
🎯 Mindset: I am becoming a data engineer!
DE Concepts & Terminology: Learn core ideas like pipelines, batch vs streaming, and data layers.
Databricks: Use a modern platform to build and manage pipelines. You can work with industry tools used in real companies.
PySpark: Process large datasets using distributed computing. You can handle data at scale, not just small files.
Remember: Concepts matter more than tools.
Always ask “why” before “how”
Learn how data flows end to end
Do not rush through platforms
It’s okay if things feel unclear at first
Real understanding will deepen later on the job
Phase 3 - Prepare & Get Hired 💼
At this point, you won’t know 100% of data engineering, and that’s completely normal. The good news is you already have around 70% of the skills needed to start applying. There’s no reason to wait anymore. Now it’s time to prepare yourself, put your profile together, and start applying for jobs.
🎯 Mindset: I am ready to enter the market!
Get Certified: Validate skills with relevant certifications. You increase trust and visibility in the market.
Build Clean Resume: Present skills and projects clearly. Recruiters quickly understand your value.
Optimize LinkedIn Profile: Make your profile searchable and attractive. Recruiters can find and contact you.
Build Portfolio: Showcase real projects publicly. You stand out beyond certificates and courses.
Start Applying for Jobs: Apply consistently and interview actively. You gain interview experience and land offers.
Improve & Learn: Fix gaps while applying and interviewing. You continuously improve until you get hired.
Remember: You don’t need 100% to start applying.
Apply before you feel fully ready
Rejections are part of the process
Treat interviews as learning sessions
Focus on explaining, not impressing
Keep improving while applying
Do not compare yourself to seniors
End of The Hiring Journey🎉
If you got a job, congratulations. I’m really proud of you and happy for you.
From here, I’ll guide you on how to grow as a data engineer and build real experience on the job.
If you’re still searching, don’t worry. You will get one. Just keep pushing.
In the meantime, you can already start looking at the next skills, and if you have time, pick a few to strengthen your profile while you keep applying.
#2 The Growth Journey
This journey is about what happens after you get hired: learning on the job, making mistakes, and growing into a strong data engineer.
Phase 4 - Junior Data Engineer 🧑💻
At this stage, you’re officially working as a data engineer, but you still won’t know everything and that’s completely normal. Most of your real learning will happen on the job. You’ll make mistakes, ask questions, fix things, and slowly start connecting the dots. This phase is about turning daily work into experience and growing step by step into a confident data engineer.
🎯 Mindset: I am learning by doing
Security Basics: Learn access control, privacy, and basic security practices. You avoid risky mistakes with data and systems.
Apache Kafka: Work with real-time streaming data. You can build and maintain streaming pipelines.
Cloud Azure & AWS: Use cloud services for storage and processing. You can deploy and run data systems in the cloud.
AI Engineering: Connect data pipelines with ML and AI systems. You support modern AI-driven products.
Remember: Making mistakes is part of the job.
Ask questions early
Focus on reliability over clever solutions
Quality is always better than speed
Learn company data and business context
Take feedback seriously, not personally
Phase 5 - Senior Data Engineer 🧠
At this stage, you’re no longer just executing tasks. You’re trusted with complex problems, bigger decisions, and guiding others. You start thinking about systems, performance, and long-term impact. Being a senior data engineer means not only delivering solutions, but helping the team move faster and smarter.
🎯 Mindset: I take ownership of complex problems and help others grow.
Code Review: Review and improve others’ code. Code quality and team standards improve.
Mentoring & Technical Leadership: Guide juniors and technical decisions. You multiply impact beyond your own tasks.
Cost & Performance Optimization: Balance speed, reliability, and cost. Systems scale without wasting money.
Advanced Data Modeling: Design scalable and analytics-ready models. Data becomes easier to use and trust.
System Design & Architecture Thinking: Design complete data systems. You solve problems at system level, not task level.
Remember: Delegate simple tasks and focus on complex problems.
Let juniors handle routine work
Take ownership of the hardest pipelines
Review code to raise the team level
Think about performance, cost, and reliability
Help others unblock faster
Phase 6 - Data Architect 🏛️
This phase is where you move from “how do we build it” to “how should this system be designed.” You focus on architecture, trade-offs, governance, and making decisions that shape the future of the data platform.
🎯 Mindset: I step back and see the whole picture.
Decision Making & Influence: Make and defend long-term decisions. You influence direction, not just execution.
Business & Technology Alignment: Align tech decisions with business needs. Your work directly supports company goals.
Data Governance & Ownership: Define rules and ownership for data. Data becomes reliable, compliant, and trusted.
Platform-Level Architecture: Design data platforms used by many teams. You shape long-term data strategy.
Remember: Your job is no longer writing code, it’s to discuss, decide, and design future systems.
Think in systems, not individual pipelines
Design how data flows end to end, not how one job runs
Decide why a system exists before how to build it
Optimize for long-term clarity, not short-term speed
Write less code, but make better decisions
So my friends,
You can start this path with no degree and no prior experience. I have seen it happen many times. The roadmap is simple. Learn the core skills, build real projects, create proof of your work, and keep improving while you apply. It takes time and patience, but it works.
If you want a clear structure to follow, you can use the full Notion Data Engineering Roadmap here → LINK
It includes all phases, resources, and guidance to help you move step by step.
Thank you for taking the time to read this. I hope it brought you clarity and helped you feel more confident about your next move.
Have a wonderful day ❤️
Baraa
Also, here are 4 complete roadmap videos if you’re figuring out where to start:
📌 Data Engineering Roadmap
📌 Data Science Roadmap
📌 Data Analyst Roadmap
📌 AI Engineering Roadmap
Hey friends —
Hey, I'm Baraa, a Data Engineer with over 17 years experience, Ex-Mercedes Benz, where I led and built one of the biggest data platforms for analytics and AI.
Now I’m here to share it all through visually explained courses, real-world projects, and the skills that will get you hired. I’ve helped millions of students transform their careers.
great!!!!!!!!!!!!!!