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In this video, I have shared my high-paying data engineering projects where I made more than $100,000+ in 1-2 years by working on these projects, these are projects that I worked with big companies/clients

Get 250+ End-To-End Projects (ProjectPro) - https://bit.ly/3GEuFgU 

Timestamps
0:00 My High-Paying Freelance Projects
0:37 250+ HandsOn Projects (ProjectPro)
1:28 Project No. 4 (Made $15,000)
6:24 Project No. 3 (Made $20,000)
10:12 Project No. 2 (Made $10,000)
11:57 Project No. 1 (Made $45,000)
15:22 You can do these projects too

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👉Data Engineering Complete Roadmap: https://www.youtube.com/playlist?list=PLBJe2dFI4sgtlK_zaqaIBdJFgieYPnQ07
👉Data Engineering Project Series: https://www.youtube.com/playlist?list=PLBJe2dFI4sgukOW6O0B-OVyX9c6fQKJ2N 
👉Become Full-Time Freelancer: https://www.youtube.com/playlist?list=PLBJe2dFI4sgtza0sAnNFwo8KPG0GcO9Il 
👉Data With Darshil Podcast: https://www.youtube.com/playlist?list=PLBJe2dFI4sgv_XmEDaXF3z1MNib7R3KUY 



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Darshil Parmar

A data engineering freelancer shares architecture overviews of real-world projects that collectively earned over $100,000. Projects covered include: an AWS-based on-premise to cloud migration using S3, AWS Glue, Redshift, and Athena; a Data Lake migration integrating Alteryx and Tableau on EC2 with AWS security services; an ML pipeline on AWS SageMaker with feature engineering and model training; and a GCP migration at Wayfair using Kafka, CDC pipelines, DataProc (Spark), and BigQuery. Key insight across all projects: the hardest part is always data ingestion and pipeline strategy, not transformation logic.

My High Paying Data Engineering Projects Explained