The Challenge

Globe Telecom is a global telecommunications and technology company headquartered in the Philippines, a $3.5B+ enterprise delivering connectivity, digital banking, and enterprise technology solutions across markets worldwide. In 2026, Globe entered what it calls its Acceleration Year: an aggressive pivot from simple connectivity to integrated, AI-led technology solutions.

At the center of that strategy sits data. Ren Batallones, Globe's Head of Data Architecture, is responsible for the foundational data assets that drive every business decision: end-to-end mapping and modeling across a complex ecosystem running on Snowflake, Databricks, and dbt Cloud.

The problem was straightforward. Demand for high-quality data pipelines was spiking. The team was facing a widening gap between the business's expectation for rapid delivery and their human capacity to build it.

Why Manual Mapping Couldn't Scale

Data mapping and modeling at Globe means translating raw source data into structured, executable business assets; pipeline work that was historically done entirely by hand.

"Much of this work has historically been manual, repetitive, and time consuming. Scaling this process the traditional way is simply impossible — it would require linear headcount growth that isn't sustainable."

Adding engineers wouldn't solve the problem, it would delay it. Globe needed a different answer.

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Why Genesis

The mandate was clear: dramatically increase delivery capacity without adding headcount. During the proof of concept, Globe tasked Genesis agents with replicating their most complex existing integration mappings against explicit success criteria: architectural accuracy, code performance, and documentation completeness.

The agents delivered, and then came the moment that settled it.

The Aha Moment

"The aha moment was seeing the agents generate a complex model in minutes that would have taken a data architect days to draft and refine. My team was initially skeptical, but when they saw the accuracy of the output against our production standards, that skepticism turned into excitement. We realized right then that we had unlocked a fundamental shift in our delivery velocity."

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The Deployment

Genesis runs natively inside Globe's existing Snowflake environment via Snowpark Container Services. Integration with Databricks, dbt Cloud, Google Drive, and JIRA gives the team flexibility to automate workflows across the platforms they already use. Globe optimized compute usage within Snowpark to maintain high throughput while controlling spend.

"Working with the Genesis team has been a true collaborative partnership rather than just a vendor relationship. They've been incredibly responsive to our feedback, treating our specific use cases as priorities for their development roadmap. Any issues we encountered were handled immediately, which gave us the confidence to move forward quickly."

Genesis now handles four core workflows for Globe:

• End-to-end source-to-target integration mapping: automated generation of complex pipelines previously requiring days of manual architect work
• dbt model generation: clean, executable, production-ready code built from business requirements
• Documentation: every output fully documented and traceable, replacing previously undocumented manual outputs
• Cross-platform workflow automation: integrated across Snowflake, Databricks, dbt Cloud, Google Drive, and JIRA

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The Transformation

What Changed

When Genesis handles the structural, syntax-intensive work of mapping and model generation, Globe's data architects stop being pipeline builders and start being architects in the full sense of the word. This way, the team gains access to work they couldn't attempt before.

"Once Genesis is fully live, our data architects will be freed from manual mapping to focus on high-value strategic modeling and architectural design. This allows us to tackle more complex data domains we previously didn't have the bandwidth to address — and ensures our team can finally keep pace with Globe's rapid delivery pace."

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ROI Summary

"Without Genesis, our only choices would have been to aggressively hire in a tight talent market or significantly slow down our delivery timelines. Neither option is viable given the speed at which Globe needs to innovate. Genesis solves this by decoupling our delivery capacity from our headcount constraints — and by accelerating the onboarding of new talent into the organization."

The team did not grow, the process was automated and the ceiling was removed.

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Beyond Data Architecture

"I envision Genesis becoming a critical tool for other technical groups at Globe that handle their own data ingestion but struggle with modeling complexity. By decentralizing the ability to generate high-quality mappings, we can democratize data preparation across the entire organization. This has the potential to become an enterprise-wide asset, not just a tool for the core data team."

That is what it looks like to scale data engineering across a global enterprise, without hiring.