Both are legitimate career choices. The right one depends primarily on which companies you want to work for — not which platform is technically superior.
Why AWS is often recommended first
AWS has broader adoption across the Indian job market — more roles across more company types list AWS skills than Azure. The free tier is generous and well-documented, which makes it easier to build and experiment without spending money. The community of AWS data engineering practitioners in India is large, which means more blog posts, tutorials, and Stack Overflow answers when you get stuck.
Starting with AWS also makes Azure easier to learn later. The underlying concepts — object storage, managed Spark, serverless compute, data warehouse services — are the same on both platforms. The service names and UI differ, but the mental model transfers.
When Azure is the smarter choice
If you are specifically targeting GCC roles, banking sector companies, or large enterprises — particularly those with Microsoft infrastructure already in place — Azure knowledge gives you a direct advantage. Azure Databricks is also one of the most widely used Spark environments in India's enterprise data space, and Synapse Analytics has strong adoption among companies running large-scale analytics on structured data.
The key is to check the job descriptions of companies you actually want to work for. If eight out of ten list Azure, that is your answer regardless of what the general market says.
The honest bottom line
Learn one well. Build something real on it. The second platform takes a fraction of the time once you know the first, because data engineering concepts are the same regardless of the provider logo on the console.