The latest articles on DEV Community by Amit Kumar Singh (@amising6). https://dev.to/amising6 https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3983416%2F9c88a36c-9ccd-4dc8-94dc-c427c5252ff4.png https://dev.to/amising6 en Amit Kumar Singh Sun, 14 Jun 2026 05:33:55 +0000 https://dev.to/amising6/from-sttm-to-snowflake-sql-building-a-metadata-driven-data-engineering-copilot-n4 https://dev.to/amising6/from-sttm-to-snowflake-sql-building-a-metadata-driven-data-engineering-copilot-n4 <p>Most data engineering teams do not struggle because they lack smart people.</p> <p>They struggle because too much of the delivery process is still repetitive.</p> <p>A source-to-target mapping document comes in.</p> <p>Then someone has to manually create:</p> <ul> <li>target table DDL</li> <li>transformation SQL</li> <li>data dictionary</li> <li>technical specification</li> <li>data quality rules</li> <li>reconciliation checks</li> <li>test cases</li> </ul> <p>For one or two tables, this is manageable.</p> <p>For a real enterprise program with many tables, changing requirements, multiple source systems, and repeated delivery cycles, this becomes a major productivity problem.</p> <p>That is the problem I am exploring with <strong>Data Engineering Copilot</strong>.</p> <p>Website: <a href="https://dataengineeringcopilot.com" rel="noopener noreferrer">https://dataengineeringcopilot.com</a></p> <h2> The idea </h2> <p>The idea is simple:</p> <div class="highlight js-code-highlight"> <pre class="highlight plaintext"><code> text Upload STTM ↓ Parse metadata ↓ Normalize into a canonical metadata model ↓ Generate engineering artifacts </code></pre> </div> dataengineering ai snowflake etl