From Mapping Document to dbt Model: Optimising Data Migration with AI

Data migration programmes often involve a series of detailed technical activities that must be executed with precision. One of the most time-intensive stages occurs after mapping documents have been approved, when transformation logic must be translated into production-ready models.

This stage is traditionally handled through manual structured query language development, requiring engineers to interpret mapping specifications, align data types, and ensure that transformation rules are implemented correctly. While necessary, the process can be labour-intensive, particularly in large migration programmes where consistency and governance are essential.

A recent proof of concept explored how this stage of delivery could be optimised through the use of an artificial intelligence agent designed to assist with translating mapping documents into production-ready dbt models. Rather than focusing on automation alone, the objective was to engineer the agent to operate in a controlled, rules-driven way that supports accuracy, governance, and repeatability within real delivery environments.

How the Optimised Workflow Operates

The workflow begins with natural language interaction through an agentic interface, allowing engineers to engage with the system while securely providing mapping schemas, target templates, and reference dbt models.

The agent then performs a structured analysis, cross-referencing field names, data types, nullability rules, and transformation logic to ensure alignment between mapping definitions and the target structure.

Before any code is generated, a validation step confirms that inputs are consistent and complete. Once validated, the agent produces Snowflake-compatible dbt models ready for engineering review.

Validated Use Cases

Model enhancement

When provided with an existing dbt model alongside a mapping document, the agent introduced new fields while preserving all existing common table expressions and transformation logic. The output passed an internal eleven-point validation framework and was ready for engineering review.

Net-new model generation

When provided with a mapping document alone, the agent generated complete staged models with verified data type alignment and full mapped field coverage.

What Optimisation Means in Practice

The agent engine was deliberately designed to:

• Perform structured analysis before generation
• Validate alignment between mapping and target structures
• Flag conflicts rather than infer assumptions
• Ensure every mapped field is represented in the final select statement
• Request clarification where ambiguity exists

This is not predictive text. It is governed generation designed to operate within enterprise data migration standards.

Impact

By optimising the agent engine rather than simply automating tasks, the manual effort required between mapping approval and model readiness can be reduced. Consistency across outputs improves, and stronger validation controls can be maintained throughout the workflow.

The engine continues to be refined through structured use cases, with insights from each iteration embedded back into the framework to support broader migration programmes.

Conclusion

Applying artificial intelligence within delivery workflows requires careful design and governance. When implemented in a structured and controlled way, it can assist data migration teams by reducing manual effort while maintaining the quality standards required for enterprise data migration.

Organisations undertaking data migration programmes may benefit from exploring practical, governed applications of artificial intelligence within delivery workflows to support greater efficiency and consistency throughout the migration process.