Gable is a data change management platform that uses static code analysis to detect changes in application code and their impact on data, providing data contracts and notifications to proactively manage data quality. Pulse 2.0 interviewed Gable co-founder and CEO Chad Sanderson to gain a deeper understanding of the company.
Chad Sanderson’s Background
Could you tell me more about your background? Sanderson said:
“I’ve spent the last decade at the intersection of data infrastructure and enterprise software, both as a marketer and product strategist. Before 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.”
Formation Of The Company
From left: James Frost (Chief Product Officer), Chad Sanderson (CEO and Co-Founder), Adrian Kreuziger (CTO and Co-Founder), and Daniel Dicker (Founding Engineer).
How did the idea for the company come together? Sanderson shared:
“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, the first shift left data management platform enabling software and data developers to iteratively build and manage high-quality data assets.”
Favorite Memory
What has been your favorite memory building Gable so far? Sanderson reflected:
“There’s nothing better than watching a customer Slack thread light up the first time Gable catches a breaking change before it hits production. It’s validation that we’re solving a real problem, not just building more dashboards.”
Core Products
What are the company’s core products and features? Sanderson explained:
“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”
Challenges Faced
Have you faced any challenges in your sector of work recently? Sanderson acknowledged:
“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.”
Evolution Of The Company’s Technology
How has the company’s technology evolved since launching? Sanderson noted:
“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.”
Significant Milestones
What have been some of the company’s most significant milestones? Sanderson cited:
- Landing a Fortune 100 financial services firm as an early design partner.
- Being adopted by one of the top employer review platforms to manage trust in a complex, multi-team environment.
- Seeing our contracts used not just for validation, but also for documenting cross-team expectations.
- Announcing our Series A and hosting a massively successful Shift Left Data conference with over 500 live attendees. You can watch the recording here.
Customer Success Stories
When asking Sanderson about customer success stories, he highlighted:
“One of our earliest customers, Glassdoor, used Gable to shift data ownership back to engineering. They now use Gable in CI to enforce data contracts, reducing support burden on the platform team and increasing confidence in published data. Another large U.S. bank expanded Gable across multiple teams after using our platform to prevent downstream outages tied to uncontracted changes.”
Funding
When asking Sanderson about the company’s funding details, he revealed:
“We’re backed by prominent investors and currently focused on scaling adoption across enterprise customers. We’re not sharing revenue metrics publicly yet, but our growth is primarily being driven by inbound interest from data leaders at Fortune 1000 firms.”
Total Addressable Market
What total addressable market (TAM) size is the company pursuing? Sanderson assessed:
“We’re targeting the enterprise data governance and platform engineering space. This market is conservatively estimated at $20B+. As data architectures decentralize and compliance pressures rise, we believe shift-left data governance will become a default requirement, not a niche solution.”
Differentiation From The Competition
What differentiates the company from its competition? Sanderson affirmed:
“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.”
Future Company Goals
What are some of the company’s future goals? Sanderson emphasized:
“We’re expanding 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.”
Additional Thoughts
Any other topics you would like to discuss? Sanderson concluded:
“The future of data governance is code-native. Just like DevOps and DevSecOps shifted responsibilities left, we’re doing the same for data. If you’re building with data at scale, the best time to enforce trust is when the code is written—not weeks later when something breaks.”
