Data Engineering Projects
Good data engineering projects are end-to-end builds that mirror real production work: ingesting raw data, transforming it, orchestrating the pipeline, and serving results to a dashboard or warehouse. The best beginner projects are local ETL pipelines using tools like dlt, DuckDB, and Polars; strong intermediate projects add orchestration (Apache Airflow), analytics engineering (dbt), and cloud infrastructure; and advanced projects tackle real-time streaming with Apache Kafka or distributed batch processing with Apache Spark. Below are 11 hands-on projects across three difficulty levels.
Build real-world data engineering experience with hands-on projects. From simple ETL pipelines to complex streaming architectures, master the skills employers are looking for.
Build Your Data Engineering Portfolio
Our project-based learning approach gives you practical experience with real-world data engineering challenges. Each project includes detailed instructions, starter code, and comprehensive solutions to help you learn effectively.
Project Categories:
- • ETL/ELT Data Pipelines
- • Real-time Stream Processing
- • Data Warehouse & Lake Architecture
- • Cloud-Native Data Solutions
- • Microservices Data Architecture
- • Analytics & Monitoring Dashboards
Technologies You'll Master:
11 projects available across 3 difficulty levels. Perfect for building a portfolio that demonstrates your data engineering expertise to employers.
What You'll Learn from Data Engineering Projects
Through these hands-on projects, you'll gain practical experience with data pipeline design, stream processing architecture, data warehousing, cloud platforms, and production deployment strategies. Each project is designed to simulate real-world scenarios that data engineers face daily.
Skills Development
- Data Pipeline Architecture and Design Patterns
- Stream Processing with Apache Kafka and Apache Spark
- Batch Processing and ETL/ELT Implementation
- Data Modeling and Warehouse Design
- Cloud Platform Integration (AWS, GCP, Azure)
- Container Orchestration with Docker and Kubernetes
- Workflow Orchestration with Apache Airflow
- Data Quality and Monitoring Implementation
- Performance Optimization and Scaling Strategies
- CI/CD for Data Engineering Workflows
Showing 11 of 11 projects
Local Data Engineering Environment with dlt, DuckDB & Jupyter
Set up a local development environment for data processing and analytics using Jupyter notebooks, dlt, and DuckDB. All tools are open-source and run locally.
Tools & Technologies:
Scheduled GitHub ETL with Polars, DLT & DuckDB
Build a scheduled ETL pipeline that extracts GitHub repository data, transforms it with Polars, and stores results in DuckDB
Tools & Technologies:
End-to-End Analytics Platform with DuckDB + Metabase
Build a modern, low-cost analytics stack using DuckDB, Metabase, and GitHub Actions for automated data updates and business-ready dashboards.
Tools & Technologies:
Infrastructure-as-Code Setup on GCP
Provision a GCP environment using Terraform with BigQuery & Cloud Storage, staying within free tier limits
Tools & Technologies:
ETL Pipeline Orchestration with Apache Airflow
Design and implement an orchestrated ETL pipeline using Apache Airflow to extract, transform, and load weather data from a public API into a data warehouse.
Tools & Technologies:
Analytics Engineering Workflow with dbt + Metabase
Build a production-grade analytics workflow: model, test, and document data with dbt, then visualize insights in Metabase.
Tools & Technologies:
GitHub Events Analytics with PySpark
Build a production-style batch data pipeline using Apache Spark to process GitHub event logs
Tools & Technologies:
⚡ Real-Time Data Streaming with Apache Kafka
Build a real-time data pipeline using Kafka (Confluent Cloud), JSON, Python, and Polars. Simulate NYC Taxi data, process in real time, and visualize with Metabase.
Tools & Technologies:
CI/CD for Data Pipelines
Build a complete CI/CD pipeline for a data engineering project using GitHub Actions, dbt, Airflow DAG testing, and Terraform infrastructure deployment.
Tools & Technologies:
Tourism Recovery Dashboard (SQL + Power BI)
Answer a real business question end to end: load Eurostat regional tourism data into DuckDB, model the metrics in SQL, and ship a one-page Power BI dashboard explaining where tourism recovered fastest between 2022 and 2025.
Tools & Technologies:
Frequently Asked Questions
What is a good first data engineering project?
A good first project is a local ETL pipeline using dlt, DuckDB, and Jupyter. It takes 2-4 hours, runs entirely on your laptop with open-source tools, and teaches the core extract-transform-load loop without cloud setup or cost.
How many projects do I need for a data engineering portfolio?
Three to five projects spanning different difficulty levels is enough for a strong portfolio: one beginner pipeline, two intermediate builds (orchestration with Airflow and analytics engineering with dbt), and one advanced project such as real-time streaming with Kafka or batch processing with Spark.
What skills do data engineering projects teach?
Hands-on projects build skills in data pipeline design, stream processing (Kafka, Spark), batch ETL/ELT, data warehouse modeling, cloud platforms (AWS, GCP, Azure), container orchestration (Docker), workflow orchestration (Airflow), and CI/CD for data workflows.
Why Choose Project-Based Learning?
Real-World Application
Work with actual datasets and scenarios that mirror production environments. Build solutions that demonstrate your ability to handle complex data challenges.
Portfolio Development
Create a compelling portfolio that showcases your technical skills to potential employers. Each project includes documentation and deployment instructions.
Industry-Relevant Skills
Focus on the tools and technologies that are in high demand in the data engineering job market. Stay current with modern data stack practices.