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
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