Learn from leading industry experts working at the world's most innovative companies
16 weeks part-time classes that adapt to your busy schedule
Learn from anywhere in our virtual classroom with live lectures and hands-on labs
25 Seats Remaining
Master the skills to become an effective data engineer with the modern data stack in 16 weeks. Take a comprehensive look at the curriculum here.
Master the core concepts and primitives used in data engineering around ETL. Abstractions and tools such as Airflow and Airbyte are built on top of the core concepts and primitives taught in this topic.
Continue down the path of mastering core concepts and primitives used in data engineering. In this topic, we learn the ELT pattern, a fairly recent addition to data engineering that was born from the recent explosion of cloud adoption.
Master the concepts to containerize, build, and deploy ETL pipelines into a production environment hosted on the cloud. Enable code versioning and team collaboration best practices through Git.
Create an end-to-end ETL pipeline using extract, load, and transform patterns covered in earlier topics.
Most businesses generate and store data in multiple systems such as Customer Relationship Management (CRM) systems, Order Management Systems (OMS), Accounting systems, Marketing platforms, and many more. Handcrafting the Extract and Load logic for each system is a tedious process that can be automated using data integration tools such as Airbyte.
As businesses scale, their data volumes increase until it is no longer viable to process data on a single compute instance. To solve scale issues, we look at technologies like Snowflake which are designed to perform analytical processing on large volumes of data.
We streamline our Transform pipeline development process using dbt, which popularized a subfield of data engineering known as Analytics Engineering.
Data engineering does not exist in a vacuum. As data engineers, we transform and model data for our end user consumption to power use-cases such as machine learning, business intelligence, and analytics. To model the data with software engineering principles of modularity and reusability, we apply data modelling techniques such as dimensional modelling. To enable end users to slice and dice models that we produce, we provide a layer on top of the data warehouse known as the semantic layer.
Spark is a distributed data processing system capable of processing large volumes of data. Databricks provides an ecosystem of tooling to enable Spark to run for multiple use-cases such as data engineering, stream processing, and machine learning.
We discover how Spark uses the separation of storage and compute to enable scale, learn about the delta file format, use Spark for data engineering, and apply data quality tests using Great Expectations.
Create an end-to-end ETL pipeline capable of processing large volumes of data.
Data orchestration enables data engineers to stitch together different parts of ETL into a single cohesive pipeline. Data orchestration makes it easy to trigger, schedule, monitor, and configure alerts for the pipelines. Data orchestrators like Airflow come with plugins or providers to connect existing tools in your data stack like Airbyte, dbt, Snowflake and Databricks, so that you can easily orchestrate steps between them.
Enable real-time insights from fast moving data. Learn the core concepts and primitives of stream processing using Kafka, and deploy kafka topics on Confluent Cloud. Integrate real-time events into Clickhouse, a real-time database, and perform data transformation in Clickhouse. Define and test Clickhouse materialized views using dbt.
As the data engineering team grows, so does the code complexity. To provide assurance that data engineers are doing the right things, automated code integration pipelines can be used to test and verify a data engineer’s code changes in a separate branch-based environment. After code has been validated, code can be automatically built and released into various deployment environments such as staging and production.
Showcase all the skills and technologies you have learnt throughout the bootcamp to future employers. Implement either a lambda or kappa architecture with ETL pipelines capable of processing large volumes of data.
Graduate with a portfolio of professional data engineering projects that you can showcase to the world. Take a look at some projects below from our most recent Data Engineering Camp cohort.
Kickstart your career in data engineering by presenting your capstone project to Data Engineers and representatives working at startups and large companies.
16 weeks, 6 July 2026 – 26 October 2026
Monday, Tuesday, and Thursday
10:00am – 1:00pm (UTC)
25 Seats Remaining
Chris Dilger, a seasoned Senior Engineer at Versent, a leading cloud consultancy, excels in building data pipelines with a focus on AWS services and Snowflake. Beyond his data engineering prowess, Chris seamlessly transitions between front and backend engineering roles, contributing to versatile solutions that bridge data architecture and application development. Formerly a Junior Consultant in Data at the Data Experience, Chris brings a wealth of experience to his current position. His dedication to crafting efficient data solutions, coupled with a passion for technology, positions him as a key contributor in the dynamic landscape of cloud consulting at Versent.
Jonathan is the founder of Data Engineer Camp. Currently a Data Engineer at Canva, John is building data platforms to empower teams to unlock insights to their products. He has previously worked at EY, Telstra Purple, and Mantel Group, where he has led data engineering teams, built nearly a dozen data platforms, and developed new products and offerings. Jonathan has taught over a hundred data professionals who are now working at leading technology companies around the world.
Douglas is an analytical engineer specializing in maintaining and building data pipelines that empower end users to uncover insights and drive business impact. Previously, he worked as a data scientist, where he made significant contributions by developing statistical models and optimizing data pipelines. He has a deep understanding of modern data platforms and extensive experience in building robust data pipelines.
Python – You are comfortable with variables, lists, dictionaries, functions, loops, conditionals, and using Python libraries.
if understand:
print("You understand the basics")
else:
print("Take some time to learn the basics")
SQL – You are comfortable with Data Manipulation Language (DML) such as select, group by, where, having, insert, delete, update. You are comfortable with Data Definition Language (DDL) such as create table, alter table.
CASE
WHEN understand=TRUE THEN 'You understand the basics'
ELSE 'Take some time to learn the basics'
END;
1:1 coaching with a career coach
Receive guidance on your data engineer career trajectory, resume review, and preparation for interviews.
1:1 expert advice from practitioners
Receive expert advice from data engineering practitioners about industry trends, technology stack tradeoffs, and professional development.
Ask questions in the live-classes and office hours and your instructor will provide answers
Ask questions in the Slack channel #help and your peers or instructors will provide answers
Work on projects in a group and hold each other accountable
Alumni slack channel
Join our alumni community slack channel and stay in touch with your peers.
Alumni events
Attend alumni-only events and network with other data engineers in the industry.
I would recommend the bootcamp to two types of people. First, people who are interested in a career as a data engineer. The course teaches you the fundamentals for building a data stack using modern data tooling in hands on, steady paced, rigorous manner. Second, people who work with data engineers and are interested in understanding the modern data stack and how different parts of the data engineering lifecycle work together. Within weeks, I was able to set up a Kafka pipeline, use dbt to transform data, and set up a CI/CD pipeline via GitHub Actions.
Paul Hallaste, Analytics Lead at Fidelity International (Japan)
I can absolutely recommend the Data Engineering Bootcamp to everyone that needs to acquire the modern data engineering skills in a fast, digestible and reliable way. In 16 weeks you will receive a perfectly developed curriculum of the most important concepts and tools in data engineering. You receive all lessons in well-built units and chapters that develop over time, and the delivery of all learning materials comes with practical examples and training sessions inside and outside of class.
Dr. Gernot G. Supp, Manging Director at Delta-Science S.L (Germany)
I really want to thank Jonathan, Jay, Rashid, Chris, and Doug for their invaluable support and expertise. This boot camp was a challenging yet incredibly rewarding experience, far surpassing any others I've attended. It provided a well-structured curriculum, excellent mentorship, and a comprehensive roadmap for anyone interested in engineering, data analysis, or data science. I highly recommend it to anyone looking to advance their skills in these fields.
Daniel Premisler, Business Intelligence and Machine Learning Developer at Pointer Israel (Israel)
If you're looking to break into the industry, I think this bootcamp is probably your best choice. It will help you to have a good understanding of how to combine all the tools together into the single pipeline. Using high and low level programming, build custom drivers and utilize the pre built ones, build the portfolio to showcase to the potential employers. It will also provide interview tips, in addition to the opportunity to introduce your project to multiple companies at the end of the bootcamp. If you're looking for a career change, welcome to the opportunity.
Mantas Liutkus, Data and Automation Engineer at M Solutions Corp (Canada)
I would 100% recommend Data Engineer Camp to both newbies to the discipline looking to change their careers, and seasoned pros looking to round out their knowledge. I learned something brand new, or went deeper on topics I already knew almost every single week. The value of that really can't be understated. The best thing about the bootcamp is definitely the level of detail. The bootcamp really went deep into spark internals, which I was really impressed by, and that level of detail was maintained across every single topic.
Alexander Potts, Data Engineer at Endeavour Group (Australia)
The best thing about bootcamp are the people. But to elaborate, the team not only work as data engineers in their day job, but they've put this bootcamp together to share their knowledge and grow the industry. Learning from such passionate teachers is an inspiration for any student. Furthermore, learning alongside students who volunteer to give up their evenings and weekends has allowed me to connect and network with eager to learn like minded industry professionals, which is invaluable in one's career.
Luke Huntley, Data Engineer at Western Power (Australia)
Get our bootcamp brochure Get our curriculum week by week Get our pricing information Speak to our enrolments team Data engineer bootcamp & course students have to be comfortable with basic Python and SQL programming concepts since the bootcamp is fast paced and we cover a lot of ground. It is also recommended that candidates have at least 1 year of working experience before enrolling.
Computer requirements
Minimum hardware requirements
Yes, to request for reimbursement, you can make a copy of our reimbursement template and send it to your manager.
Yes, students that complete 2 out of 3 projects with a passing grade will receive a certificate of completion.
The course is delivered virtually through Zoom for flexibility of our students. Our Zoom class consist of live lectures and hands-on labs with instructor guidance. Slack is used for student and instructor communications.
All classes are recorded in the event you are not able to make it to class.
We cover topics such as network security and access control when provisioning access to the resources that we deploy on the cloud. We do not cover the topic of migration directly e.g. how to migrate an on-premise data platform to the cloud. However, we can discuss such topics during office hours.
Yes, the course covers DataOps principles such as Data Quality testing and monitoring, and Continuous Integration and Deployment.
We offer career services which include one-on-one mentoring, data engineering application creation tutorial, data engineering technical interview seminar and access to instructors for career guidance throughout the course. Unfortunately, we cannot promise a job but we can promise to build your data engineering skillset with a modern techstack and develop your career application.
Instructors are typically available during live classes, office hours, and via online communication platforms (Slack) for personalized guidance and support.
Instructors at our data engineering bootcamp usually have a mix of industry experience and teaching backgrounds. They may have taught at universities, conducted workshops, mentored junior engineers, or worked as trainers in their previous roles. Their diverse backgrounds ensure a comprehensive and practical learning experience for students.
Data engineering courses offer comprehensive, shorter-term education suitable for beginner experience levels, while data engineering bootcamps provide intensive, hands-on training focused on practical skills for rapid career entry or upskilling.