Chad Sanderson, CEO of Gable.ai, is a prominent figure in the data tech industry, having held key data positions at leading companies such as Convoy, Microsoft, Sephora, Subway, and Oracle. He is also the author of the upcoming O’Reilly book, “Data Contracts” and writes about the future of data infrastructure, modeling, and cotracts in his newsletter “Data Products.”

What is Gable?

Gable is the first shift left data management platform, enabling developers to build and manage high-quality data assets from the start. Gable’s platform bridges the critical gap between data teams and developers to ensure that data is accurate, reliable, and actionable across organizations. Gable’s “Shift Left” approach embeds data quality and governance early in development, akin to how DevOps streamlined coding. Instead of data teams spending time on cleaning up messes later, the treatment of data is a shared responsibility with developers upfront using clever API-based data contracts. The result? No more broken pipelines or endless debugging. 

What’s your background, and why did you start Gable?

I’ve spent the last decade at the intersection of data infrastructure and enterprise software, both as a marketer and product strategist. Prior to Gable, I worked with companies like Convoy and Microsoft, solving complex data challenges—from open source databases to cloud-native observability tools. The throughline has always been helping teams operationalize trust in constantly changing systems.

The inspiration for Gable started with a simple observation: data issues don’t start in the warehouse—they start in the code. Our founding team saw this pattern repeatedly: platform teams building the right abstractions, but data still arriving broken, misaligned, or late. We wanted to move the accountability upstream, giving teams the ability to enforce expectations at the point of creation, not after the fact. That’s what led us to build Gable.

What challenges have you encountered in your space, and how have you addressed them?

The biggest challenge is organizational, not technical. Convincing teams that governance doesn’t have to be slow. We’ve overcome that by meeting engineers where they already work—GitHub, CI, code review—and making enforcement feel like a natural extension of their workflow.

What does success look like for your customers? 

Early adopters of Gable have seen faster resolution of data issues across the board, with initial results suggesting incident resolution time can improve by up to 70%, while development cycles for data-dependent features accelerate by nearly 50%.

A more specific example is that Glassdoor used to face multiple critical data incidents every month, slowing down development and forcing our data engineers into constant troubleshooting mode. Developers had no visibility into how their changes impacted downstream consumers, creating bottlenecks and inconsistencies. Now that they use Gable’s data contracts, they have gone three months without a single incident—something they hadn’t achieved in years. Their engineers have clear guardrails, data quality is built into development, and their teams spend more time on strategic analytics and AI instead of fixing broken pipelines.

How is Gable different from other solutions in the data governance space?

Most tools wait until data lands in the warehouse to validate or monitor. Gable enforces trust earlier—at the code level—before data ever hits production. We treat data like software, embedding governance into CI/CD. That’s a fundamentally different approach from catalog-first or lineage-first tools.

Gable sits at the intersection of CI/CD and data governance. We analyze code-level changes—like serialization logic, type shifts, or timestamp conversions—to detect and prevent data-breaking changes before they hit downstream systems. Our platform supports:

• Static analysis of application code (TypeScript, Java, Python, Swift)
• CI enforcement of data contracts
• Data Dependency Mapping
• Governance and Policy as Code
• Integration with tools like GitHub, Snowflake, dbt, and Kafka

We started with schema diffing and code analysis. Since then, we’ve added native CI/CD integrations, contract versioning, automated rollback safety checks, and dynamic data diffing for downstream impact analysis. The goal is always the same: catch it early, fix it fast, move on.

What are some future plans or directions for Gable?

We’re using our recently announced Series A to expand deeper into CI/CD workflows, enabling progressive delivery for data changes. We’re also focused on enhancing our impact analysis engine, so teams can predict who and what will break before they hit merge. In the long term, our goal is to make “data-aware development” the default.

Gable is also committed to help educate the industry and recently held our inaugural Shift Left Data conference with over 500 live attendees. You can watch the recording here and join us next year. 

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