Most data engineering teams do not struggle because they lack smart people.

They struggle because too much of the delivery process is still repetitive.

A source-to-target mapping document comes in.

Then someone has to manually create:

• target table DDL
• transformation SQL
• data dictionary
• technical specification
• data quality rules
• reconciliation checks
• test cases

For one or two tables, this is manageable.

For a real enterprise program with many tables, changing requirements, multiple source systems, and repeated delivery cycles, this becomes a major productivity problem.

That is the problem I am exploring with Data Engineering Copilot.

Website: https://dataengineeringcopilot.com

The idea

The idea is simple:

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Upload STTM
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Parse metadata
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Normalize into a canonical metadata model
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Generate engineering artifacts