Curriculum at a glance
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Next course: April 27, 2026
Data Engineering Bootcamp
Learn core data engineering technologies, tools, and platforms — and gain hands-on experience to build and manage scalable data infrastructure in the cloud.
Full-time | 16 Weeks | Remote
No money? No problem. Get 100% financing with the Agentur für Arbeit
Build the data infrastructure of tomorrow
Design robust data pipelines, automate workflows, and work with tools like Docker, Kafka, Spark, and AWS. In our Data Engineering Bootcamp, you'll learn how to create scalable solutions for modern businesses – hands-on, intensive, and future-focused.
Course overview
What you'll learn at our Data Engineering Course.
Programming and Data Modeling
Kickstart your journey with advanced Python programming and data modeling techniques. You’ll dive deep into data structures, functional coding, performance tuning, and error handling — then transition into advanced SQL queries, relational modeling, normalization, and optimization strategies. Your learning journey starts with more than just tech skills – it begins by building a strong foundation for success. In the first week, you’ll connect with your peers, learn how to work effectively in the course, explore the IT industry and career opportunities, and discover how to use (and not misuse) AI as a tool to support your growth.
Containers and Workflow Orchestration
In this phase, you'll containerize your apps with Docker and learn how to manage workflows using Airflow and Prefect. You’ll also be introduced to dbt to build modular, testable transformations in your data pipelines.
Cloud Data Engineering with AWS
Learn the fundamentals of cloud computing with AWS. You’ll build cloud-native pipelines using Glue, Lambda, and S3, and explore data warehousing with Redshift. Topics include identity and access management, cost optimization, and best practices in cloud architecture.
Big Data and Real-Time Processing
Work with large-scale datasets using Spark and PySpark, and implement real-time data solutions using Kafka. You’ll also gain hands-on experience with NoSQL databases like MongoDB and understand batch vs. stream processing use cases.
Governance, DevOps, and Capstone
In your final phase, you’ll integrate all your knowledge into a capstone project — building and presenting a full-stack data platform. You’ll also learn about data governance, metadata, and quality frameworks, while using GitHub Actions and Terraform to automate and deploy infrastructure. This phase includes AWS certification prep and job readiness support.
Spiced info events
If you're interested in our further training and would like to learn more before you begin, please feel free to attend one of our information sessions and see which topic is right for you.
PyBerlin Meetup @ Spiced
Get event detilsPython
Learn the foundations of one of the most popular programming languages in data — used for data manipulation, automation, and building data pipelines.
SQL
Master the language of relational databases to efficiently store, query, and analyze large datasets.
Docker
Containerize your applications and make data pipelines portable, reproducible, and scalable — locally and in the cloud.
Apache Airflow
Plan, monitor, and automate complex data workflows using one of the most popular orchestration tools.
Apache Spark
Process massive datasets in real time using this powerful distributed computing framework and its Python API, PySpark.
AWS
Work with essential cloud services like S3, RDS, Glue, Lambda, and Redshift to run efficient data workflows in the cloud.
MongoDB
Learn how to store and retrieve unstructured data using flexible NoSQL solutions like MongoDB.
Kafka
Build streaming applications and understand how to process real-time data using Apache Kafka.
Terraform
Automate cloud infrastructure deployment using Infrastructure as Code with Terraform.
GitHub Actions
Implement CI/CD pipelines for your data engineering projects directly from your GitHub repository.
Career Service
Ready for the real world.
What good are skills without getting a foot in the door? We focus extensively on helping you ace real world technical interviews.
Coaching built for you.
We believe that development is continuous, so we offer up-to-date career coaching sessions to help you progress professionally.
Beyond the technical.
Changing careers is more than learning new tech skills. We additionally provide you with spot on soft skills to ace your application process.
Your next step.
Wondering ‘what’s next’? We're connected with exciting startups and companies in Germany.
Where Spicedlings work
Spicedlings are getting hired by your favourite companies:
Financing Options
Invest in your future
Bildungsgutschein
If you’re registered as unemployed (or soon to be) in Germany, you could be eligible to have all your costs covered with a Bildungsgutschein (training voucher).
For more information on this option, check our page dedicated to financing your coding bootcamp with a Bildungsgutschein.
Deferred Payment
We want to make our best-in-class tech courses available to everyone with the motivation to complete them.
Our Deferred Payment Option enables those who aren’t in the position to pay upfront nor in instalments to participate, by offering the chance to pay back at a later date.
Full Payment
If you’re ready to cover the cost of our coding bootcamps immediately, this is the option for you. Pay 14 days before the course starts.
Application & Dates
16 Weeks | Full-time | English
FAQ
Who is the Data Engineering Bootcamp suitable for?
The Bootcamp is ideal for career changers, graduates and professionals from fields such as analytics, software, finance or marketing who want to get into data processing. You don't have to be a developer – we will provide you with preparatory material. If you are passionate about data and want to build scalable solutions, this Bootcamp is for you.
What will I have learned by the end of the Bootcamp?
By the end of the Bootcamp, you’ll know how to design, build, and manage data pipelines and infrastructure using industry-standard tools and platforms like Python, SQL, AWS, Docker, Spark, and Kafka. You’ll be able to automate workflows, handle large datasets, and deploy cloud-native solutions. You’ll also complete a capstone project to showcase your skills and prepare for the AWS Certified Data Engineer – Associate exam.
What are my job opportunities after this Bootcamp?
Graduates of the Data Engineering Bootcamp are qualified for roles such as Data Engineer, Cloud Data Engineer, ETL Developer, Big Data Engineer, or Platform Engineer. As more companies invest in cloud infrastructure and data-driven decision-making, demand for skilled data engineers continues to grow across industries. You'll be equipped for positions in startups, large enterprises as well as cloud-focused teams.
How much can I earn as a Data Engineer?
Starting salaries in Germany are around €40,000 with median around €70,000. With experience, specialization in tools like AWS or Spark can increase your earning potential quickly.
What kind of foundational skills should I build before starting a Data Engineering Bootcamp to avoid feeling overwhelmed?
That's a great question. Data Engineering involves complex pipelines, databases, and cloud platforms, so prepping early helps you stay on track.
💡 Here’s a solid pre-bootcamp checklist: SQL basics: Understand SELECT, JOIN, WHERE, GROUP BY. Platforms like Mode Analytics or SQLBolt are beginner-friendly. Python fundamentals: Grasp lists, dictionaries, functions, and file handling—ideal for ETL scripting later. Command-line familiarity: Basic Bash commands like cd, ls, grep, piping—used when working with Linux-based data nodes. Data modeling concepts: Know what tables, schemas, and normalization mean.
💬 Grad testimonial: “I spent two weeks pre-bootcamp practicing SQL and Python. When we started with Airflow and Spark, I felt less lost and more confident.”
Typically, full-time bootcamp durations range from 12 to 16 weeks — how prepared you are before the first line of code won't affect the duration, but can seriously improve your experience. A little prep goes a long way. ✅
What typical infrastructure and tools will I use during a Data Engineering Bootcamp? Will this match what companies use?
Excellent curiosity — knowing the tools ahead of time gives you an edge.
🔧 Common industry-aligned tools taught include:
- Relational Databases: PostgreSQL, MySQL or Amazon RDS.
- Data Warehousing: BigQuery, Snowflake, Amazon Redshift.
- ETL/ELT Pipelines: Apache Airflow or AWS Glue to manage workflows.
- Large-scale processing: Spark (PySpark) on cloud or Databricks.
- Cloud Platforms: AWS, GCP or Azure used to spin up data storage, compute clusters, and data lakes.
- Version control: GitHub for project collaboration and CI/CD pipelines.
💬 Real bootcamp insight: “By Week 3 we were building Airflow DAGs on AWS, processing data in Spark clusters, and loading it into Redshift - just like professional data teams.”
These tools are widely used by companies in finance, logistics, and SaaS. Bootcamp graduates frequently report employers recognizing and valuing the tech stack they learned.
What our alumni say
Join our growing tech community and become a certified Data Engineer in just 16 weeks.
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