COURSE & TRAINING
Data Engineering
Build scalable data platforms with modern warehousing, batch and streaming pipelines, orchestration, and data transformation workflows.
Online
CHF 1'250
Live, instructor-led public or private delivery.
On-site
CHF 2'500
On-site delivery for team or public cohorts.
Private team cohort
On request
Private delivery tailored for your engineering team.
Who this training is for
Data Engineers
Deepen practical skills across warehousing, transformation, orchestration, Spark processing, and pipeline design.
Software professionals interested in data platforms
Build a bridge from software engineering into modern data platform architecture and tooling.
Researchers interested in data engineering and management systems
Connect data management concepts with hands-on warehouse, processing, and orchestration workflows.
What you'll learn
Understand data engineering foundations
Learn the responsibilities, system architectures, and modeling approaches behind modern cloud data platforms.
Build batch and transformation pipelines
Use Spark, dbt, and Airflow to ingest, transform, orchestrate, and benchmark scalable data workflows.
Work with modern warehousing patterns
Design Snowflake-based environments, security configurations, and structured data models for practical use cases.
Explore advanced topics and reporting
Connect streaming patterns, visualization tooling, and capstone integration work into a complete data platform perspective.
PREREQUISITES
What participants should bring
Participants should have at least one year of programming experience, basic SQL knowledge, comfort with the command line, and ideally some familiarity with Python.
PREPARATION
Before the course starts
Participants should be ready for hands-on work across warehousing, transformation, Spark-based processing, orchestration, and capstone pipeline exercises.
CURRICULUM
Inside Data Engineering
This course is available online, onsite, and as a private team cohort. Public sessions proceed subject to minimum participant numbers, and private versions can be adapted to the participant's preferred warehouse and orchestration stack.
Data Engineering Foundations
Day 1
Architectural foundations, Snowflake, modeling concepts, and first transformation workflows.
Data Engineering Foundations
Day 1
Architectural foundations, Snowflake, modeling concepts, and first transformation workflows.
Theoretical Part
- Core responsibilities of data engineers
- Data system architecture
- Emerging industry trends
- Key performance metrics
- Cloud data warehousing concepts
- Relational and dimensional modeling
- Star and snowflake schemas
- Normalization techniques
Practical Part
- Configure a Snowflake environment with security and access control
- Create an initial data model
- Set up dbt for transformation workflows and version control integration
- Prepare the development environment
Data Processing and Workflow Management
Day 2
Distributed processing, ingestion patterns, orchestration, and practical pipeline construction.
Data Processing and Workflow Management
Day 2
Distributed processing, ingestion patterns, orchestration, and practical pipeline construction.
Theoretical Part
- Apache Spark overview
- Distributed computing principles
- Performance optimization strategies
- DataFrame operations and RDD transformations
- Data ingestion patterns
- Apache Airflow fundamentals
- DAG design, scheduling, and dependency management
Practical Part
- Implement Spark data transformations
- Work with Spark and Snowflake integration patterns
- Build an end-to-end batch pipeline using Spark, dbt, and Airflow
- Apply code review, performance benchmarking, and optimization techniques
Advanced Topics and Capstone Project
Day 3
Streaming, reporting, and capstone integration across multiple tools.
Advanced Topics and Capstone Project
Day 3
Streaming, reporting, and capstone integration across multiple tools.
Theoretical Part
- Real-time data concepts
- Spark Streaming architecture
- Event-driven architectures
- Visualization tool ecosystem including Tableau and Looker
- Interactive dashboard design
- Advanced learning paths
Practical Part
- Explore streaming and event-driven processing patterns
- Extract and visualize data with Snowflake-backed reporting flows
- Develop a comprehensive multi-tool data pipeline
- Review, optimize, and present the final capstone solution
TECHNOLOGIES
Technologies used in Data Engineering
These are the tools used in the public version of the course. For private cohorts, we can swap the stack and tune the flow to your team.
Need Data Engineering for your team?
Private cohorts can be run online or on-site, adapted to your tool stack, and reshaped into a half-day, multi-day, or mixed-course program for your team.
COURSE INSTRUCTORS
Sonia Boussabeh
Instructor - Data & ML Engineering
Imen Bakir
Instructor - Data Engineering
Upcoming cohorts
Date / PeriodSession 1: July 2026Online — Online via Teams
StatusOpen
Actions
Date / PeriodSession 2: October 2026Online — Online via Teams
StatusOpen
Actions
Frequently asked questions
Everything you need to know about Data Engineering.
Which core technologies are covered in the Data Engineering course?
The course centers on Snowflake, Apache Spark, dbt, and Apache Airflow, with supporting discussion of visualization and real-time processing tooling.
Is the Data Engineering course suitable for teams modernizing existing pipelines?
Yes. The course is designed for practical data platform work and is especially relevant for teams improving warehouse design, transformation workflows, orchestration, and scalability.
Can the Data Engineering course be adapted to our data stack?
Yes. Private delivery can emphasize the organization's preferred warehouse, orchestration patterns, transformation tooling, and target architecture.