Meet Alex, a data engineer responsible for turning business requirements into reliable, production-ready data pipelines. A new request arrives, and he begins the familiar process of understanding the requirement, identifying relevant data sources, mapping fields, developing transformation logic, validating outputs, and preparing the code for review. One pipeline may be manageable, but when the same sequence must be repeated across dozens of pipelines, the effort quickly piles up.
Not all pipelines are equally complex. Some require careful interpretation of business rules, while others follow patterns that engineering teams have implemented many times before. Yet both can demand considerable manual effort. As data ecosystems expand and requirements multiply, engineering teams face a growing challenge: how can they accelerate delivery without compromising the quality, consistency, and reliability of their data pipelines?
Agentic ETL offers a way to rethink this process. With Databricks Genie, AI-assisted capabilities can streamline routine engineering activities and move pipelines from business requirements to validated, review-ready outputs.
The Challenge of Repetitive Pipeline Development
As pipeline demand grows across business functions, markets, and source systems, this manual approach becomes increasingly difficult to scale. Delivery cycles lengthen, engineering capacity comes under pressure, and maintaining consistent code quality across pipelines becomes more challenging.
Automation can help address these challenges, but generating technically valid code does not automatically guarantee a reliable outcome. Metadata may describe the structure of a source without capturing its business meaning. Transformation logic may depend on an intended data grain or source-specific rules that cannot be inferred from metadata alone.
A structured workflow can address these limitations by establishing clear requirements, applying engineering standards, and incorporating validation checkpoints throughout development.
Redefining the Data Engineer’s Role with Databricks Genie
Databricks Genie enables a more coordinated approach to data pipeline development, supporting engineers across the development lifecycle rather than treating AI as a standalone code-generation tool. This shifts the focus from automating isolated tasks to organizing development work around the respective strengths of AI and engineering expertise.
Consider Alex working alongside Genie. Genie supports routine development activities and helps move pipeline development forward, while he interprets business requirements, reviews generated logic, and validates the results. When a requirement is ambiguous or a transformation depends on business context, he remains responsible for determining the appropriate approach.
To make this collaboration effective, the workflow must follow established engineering practices. Defined standards guide development, iterative execution allows certain issues to be identified and addressed, and human review provides checkpoints for decisions that require technical expertise. These practices help integrate AI into the engineering process while maintaining the quality controls and accountability required in enterprise environments.
The value of this approach becomes clearer when applied to real-world engineering demands, where pipeline volume, source complexity, and delivery timelines put these principles to the test.
Agentic ETL in Practice: A Global CPG Enterprise
For a global CPG enterprise operating across more than 80 countries, manual pipeline development was creating challenges for delivery speed, scalability, and consistency. MathCo applied an agentic ETL approach using Databricks Genie within the enterprise’s UK Sales and Revenue Management (SRM) domain, supporting the development of approximately 22 pipelines.
The workflow used seven sequential Genie skills to support the process from business requirements through development and pull-request creation. Designed to be reusable and source-agnostic, the skills followed established engineering practices and incorporated retry loops to help identify and resolve development issues through iterative execution.
Human oversight remained part of the process, particularly where business intent, data grain, or source-specific logic could not be reliably inferred from metadata. All seven skills were validated against a live source, keeping the workflow grounded in the client’s data environment.
The implementation reduced the development time for the 22 pipelines from five weeks to two weeks, with a reported productivity gain of approximately 70%. The reported cost ranged from $4 to $7 per pipeline run, depending on pipeline complexity and the number of iterations required.
The results illustrate how agentic ETL can change the distribution of engineering effort. Rather than concentrating primarily on repetitive pipeline authoring, engineers can devote more attention to reviewing outputs, validating results, and resolving issues that require judgment.
To learn more about this implementation, read the full case study.
Toward a Human–AI Engineering Model
The goal is not to take humans out of the pipeline. It is to take humans out of the parts of the pipeline that do not need their judgment.
With Agentic ETL on Databricks Genie, engineers can spend less time on repetitive development and more time on validation, complex decisions, and delivering reliable data products.
Learn more about MathCo’s Databricks partnership and solutions.
