Data Engineering Bootcamp & Certificate
Aki Wijesundara, PhD
AI Founder | Google AI Accelerator Alum
Manu Jayawardana
AI Advisor | Co-Founder & CEO at Krybe
Build reliable data pipelines from scratch.
Many aspiring data professionals get stuck at the start: they know what analytics and AI can do, but struggle to transform raw data into structured, actionable pipelines. Without solid fundamentals, projects stall, dashboards break, and AI models train on outdated or incomplete data.
This course guides you from zero to production-ready thinking. Over one month, you’ll learn to:
Collect, clean, and organize raw data efficiently
Design simple yet scalable pipelines for analytics and AI
Use tools and frameworks that mirror real-world team workflows
Debug, monitor, and maintain pipelines confidently
By the end, you won’t just understand the theory. You’ll build functioning pipelines, know how data flows from source to insight, and gain the confidence to contribute meaningfully to any data project — setting the foundation for more advanced engineering, analytics, or AI work.
What you’ll learn
Build, manage, and scale data pipelines from scratch — even if you’ve never coded one before.
Learn how to connect to common data sources (CSV, APIs, databases)
Clean and transform raw data into structured formats
Establish repeatable processes for consistent data ingestion
Understand batch vs streaming pipelines and when to use each
Build end-to-end workflows using beginner-friendly tools
Structure pipelines for reliability and maintainability
Transform messy or inconsistent data into analytics-ready tables
Automate workflows to reduce manual intervention
Ensure data accuracy and integrity across processes
Who this course is for
For aspiring data professionals who want to learn how to build and manage data pipelines from scratch, even with limited coding experience.
For professionals looking to transition into data engineering and gain practical, hands-on skills to contribute confidently to analytics.
For early-career engineers and tech-curious learners who want foundational data engineering skills to contribute to real-world pipelines.
What's included
Live sessions
Learn directly from Aki Wijesundara, PhD & Manu Jayawardana in a real-time, interactive format.
Project-based learning
Work on practical exercises that simulate real-world data workflows from ingestion to analytics-ready tables.
Downloadable resources
Access code templates, cheat sheets, and pipeline examples to practice and reuse after the course.
Community of learners
Collaborate, ask questions, and share insights with peers in a supportive learning environment.
Certificate of completion
Showcase your data engineering skills to employers, clients, or your team.
Guided workflow playbooks
Step-by-step guides for building reliable pipelines using best practices and beginner-friendly tools.
Maven Guarantee
This course is backed by the Maven Guarantee. Students are eligible for a full refund up until the halfway point of the course.
Course syllabus
4 live sessions • 20 lessons
Week 1
Feb
4
Introduction to Data Engineering Concepts
Introduction to data engineering concepts
Data transformation techniques (ETL fundamentals)
Week 2
Feb
11
Designing Scalable Pipelines
Designing scalable pipelines
Schedule
Live sessions
1 hr / week
Participate in focused, hands-on live sessions where you’ll learn core data engineering concepts, build pipelines step-by-step, and get real-time guidance from the instructor. Each session combines instruction, demos, and interactive exercises to reinforce learning.
Wed, Feb 4
6:00 PM—7:30 PM (UTC)
Wed, Feb 11
5:30 PM—7:00 PM (UTC)
Wed, Feb 18
5:30 PM—7:00 PM (UTC)
Wed, Feb 25
5:30 PM—7:00 PM (UTC)
Projects
1 hr / week
Complete practical exercises and mini-projects that simulate real-world data workflows. Apply what you learn in live sessions to build, transform, and automate pipelines, ensuring you gain hands-on experience that prepares you for production environments.
$999
USD
10 hours left to enroll