5 Must-Have Data Engineering Projects to Break Into the Industry in 2025

* Ready to Become a Data Engineer in 2025?

If you're serious about breaking into data engineering this year, your portfolio needs more than just theory — it needs real-world, scalable projects that demonstrate your ability to build, automate, and optimize data pipelines.

Here are five standout projects that will elevate your resume and impress recruiters:

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1.Goodreads Data Pipeline – End-to-End ETL with Spark & Airflow

This project builds a complete data pipeline using Goodreads API data. You’ll create:

• A data lake for raw ingestion
• A data warehouse for structured storage
• An analytics layer for insights

ETL jobs are written in Apache Spark and orchestrated with Airflow, scheduled every 10 minutes.
Skills Gained: Real-time ingestion, API integration, Spark transformations, Airflow scheduling
GitHub Repo: san089/goodreads_etl_pipeline




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2. Reddit Data Engineering Pipeline – Cloud-Scale ETL with Redshift

This project offers a robust ETL solution for Reddit data using:

• Apache Airflow for orchestration
• Celery for task distribution
• PostgreSQL, Amazon S3, Glue, Athena, and Redshift for cloud data warehousing

It’s ideal for learning how to manage large datasets and build scalable pipelines in AWS.
Skills Gained: Cloud ETL, distributed processing, Redshift optimization

GitHub Repo: airscholar/RedditDataEngineering




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3. YouTube Analytics Pipeline – Video Data Insights at Scale

This project focuses on analyzing structured and semi-structured data from YouTube:

• Extracts video metadata and trending metrics
• Performs transformations and builds analytics dashboards

You’ll learn how to handle large datasets, derive insights, and optimize for performance.
Skills Gained: Semi-structured data handling, video analytics, transformation logic
GitHub Repo: darshilparmar/dataengineering-youtube-analysis-project




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4. Streamify – Real-Time Streaming Pipeline for Music Events

Simulating a music streaming service, this project uses:

• Kafka for event ingestion
• Spark Structured Streaming for real-time processing
• dbt, Docker, Airflow, Terraform, and GCP for orchestration and deployment

It’s perfect for mastering streaming data and cloud-native engineering.
Skills Gained: Real-time data processing, cloud deployment, streaming architecture
GitHub Repo: ankurchavda/streamify




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5. RSS Feed Data Pipeline – Semi-Structured ETL with MongoDB & Elasticsearch

This project processes RSS feeds end-to-end:

• Extracts and transforms semi-structured data
• Loads into MongoDB and Elasticsearch
• Uses Airflow and Kafka for automation and scalability

It’s a great way to understand unstructured data workflows and search-based analytics.
Skills Gained: RSS parsing, NoSQL integration, automated ETL
GitHub Repo: ankurchavda/rss-feed-data-pipeline




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* Final Takeaway

Each of these projects showcases a different facet of data engineering — from batch ETL and cloud warehousing to real-time streaming and semi-structured data processing. Building and documenting them will not only sharpen your skills but also give you a competitive edge in interviews.