Here are high-quality websites and repos with ready data‑engineering project examples (end‑to‑end, mini‑projects, templates and curated ideas). Short note after each about what you’ll find there.

• GitHub — ProjectPro / Data‑Engineering‑Projects: solved, end‑to‑end data engineering projects (AWS/Azure/GCP, Spark, Kafka, ETL, streaming). Good for complete, runnable examples. (GitHub.com)
• GitHub — “awesome” / open‑source lists (gunnarmorling, devhau, eric‑erki): curated lists of data‑engineering tools and OSS projects you can study or use in projects. Great for discovering technologies and example repos. (GitHub.com)
• Garage‑Education — data‑engineering‑projects: practical, scenario‑based project descriptions (COVID, e‑commerce, weather, dbt examples, Spark log processing) with objectives and datasets. Good for portfolio projects. (GitHub.com)
• GitHub — blakecodex / data_engineering_fundamentals: mini‑projects covering ETL, Spark, data modeling, Kafka, Snowflake — good for learning core concepts through hands‑on tasks. (GitHub.com)
• Databricks / example repos and templates (sample Databricks projects, DLT templates, notebooks): notebook‑centric, production‑oriented examples using Spark/Delta Lake/DBX. Useful if you want cloud/Spark + data engineering best practices. (GitHub.com)
• GitHub — data‑engineering‑helpers / Databricks‑examples: multiple Databricks notebooks and examples illustrating Spark workflows and engineering patterns. (GitHub.com)
• Kaggle — datasets + user notebooks (search “data engineering” or specific pipeline projects): many community notebooks that show ingestion, cleaning, transformation and small pipelines you can adapt. Useful for datasets + runnable examples. (GitHub.com)
• Medium / Towards Data Engineering and other Medium lists: numerous curated lists and how‑to posts describing 5–10 end‑to‑end projects (Airflow + Spark + Kafka + dbt + Snowflake etc.) and step‑by‑step guides. Good for ideas and walkthroughs. (Medium.com)
• “Awesome Data Engineering” style GitHub repos (community curated): links to demo projects, tooling, example pipelines, and learning resources — very useful to assemble a multi‑tool project. (GitHub.com)
• Project templates & educational repos (sample‑Databricks‑Python, Databricks‑projects): reproducible templates showing CI/CD, testing, and real dataset pipelines — good as a scaffold for portfolio projects. (GitHub.com)
• Personal/toolbox pages (Arun Pandey’s Data‑engineering‑tools, similar repos): collections of smaller projects (Kafka streaming, PySpark, Docker, CI) useful for focused practice on one technology. (arunp77.GitHub.io)

How to use these:

• If you want end‑to‑end portfolio projects: start with Garage‑Education or ProjectPro repos (they give scenario, data sources, and steps). (GitHub.com)
• If you want many small examples & tooling choices: browse the “awesome” lists and Data‑engineering‑helpers Databricks examples. (GitHub.com)
• If you want runnable notebooks on real datasets: search Kaggle for “pipeline”, “Airflow”, “Spark”, or look at Databricks sample repos. (GitHub.com)

If you’d like, I can:

• pick 3 projects (beginner → intermediate → advanced) from these sources and give a step‑by‑step plan you can implement, or
• fetch direct repo links for any specific stack you prefer (Airflow + Spark, Kafka + Flink, Snowflake + dbt, cloud provider, etc.).