This content originally appeared on DEV Community and was authored by Amit Kumar Singh
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:
text
Upload STTM
↓
Parse metadata
↓
Normalize into a canonical metadata model
↓
Generate engineering artifacts
This content originally appeared on DEV Community and was authored by Amit Kumar Singh
Amit Kumar Singh | Sciencx (2026-06-14T05:33:55+00:00) From STTM to Snowflake SQL: Building a Metadata-Driven Data Engineering Copilot. Retrieved from https://www.scien.cx/2026/06/14/from-sttm-to-snowflake-sql-building-a-metadata-driven-data-engineering-copilot/
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