In the cybersecurity world, speed, accuracy, and absolute data security are non-negotiable. So when Sophos, a global cybersecurity leader protecting 600,000 organizations, faced the massive task of modernizing its data infrastructure during the largest acquisition in its history, the team needed a breakthrough.
Sophos was managing over 800 production pipelines across two aging legacy systems, including Informatica. The infrastructure was stretched to its limits, and traditional manual migrations simply weren’t fast enough. To complicate matters, Sophos’s internal GenAI Council had a strict, zero-tolerance policy: no customer or employee data could ever be sent to a third-party AI tool.
The solution wasn’t just another ETL pipeline; it was Maia.
Because it operates as a Snowflake Native App, pipeline execution happens entirely inside Sophos’s own secure cloud environment. No data ever leaves their perimeter. With security cleared, Sophos fundamentally transformed its engineering workflows by scaling agentic data engineering.
By utilizing Maia’s modular “Skills” to enforce engineering standards during the build process, Sophos automated the entire pipeline lifecycle, design, development, documentation, and testing.
The impact has been immediate and massive:
- 4x Faster Delivery: End-to-end pipeline delivery dropped from 10–16 hours to just 2–5 hours.
- Rapid Prototyping: Proof-of-concept pipelines that used to take 2–3 days are now delivered in under 1 hour.
- Unmatched Quality: The team achieved a 95%+ first-time deployment success rate and 100% standards compliance on all generated pipelines.
By integrating these AI-generated pipelines into a Git-backed CI/CD workflow, Sophos didn’t just speed up code generation, they eliminated the operational bottlenecks that usually slow down scaling.
As Jason Mulvin, Director of Enterprise Data at Sophos, put it: “The role of the data engineer is elevating. The blocking and tackling of ETL development goes away. They’re free to look more at the big picture.”
Now, Sophos is looking ahead, paving the way for citizen developers and preparing to feed governed, production-ready data to a new wave of AI applications and autonomous agents.
What makes this transformation especially significant is that Sophos didn’t have to compromise security to move faster. In security-sensitive environments, AI adoption can stall because teams cannot risk exposing proprietary, customer, or employee data to external tools. Sophos addressed that concern at the architectural level by keeping pipeline execution within its own Snowflake environment.
The shift also changed how data engineers spend their time. Instead of repeatedly building standard ETL components, writing documentation, and handling routine testing, engineers can focus on architecture, data quality, governance, and complex business requirements. Maia’s Skills help maintain consistent engineering practices across pipelines, reducing the variation that can appear when hundreds of workflows are developed manually.
For an organization managing more than 800 production pipelines, that consistency matters. Applying the same engineering standards across hundreds of pipelines can reduce deployment failures, rework, and ongoing maintenance demands. Combined with Git-backed version control and CI/CD processes, agentic data engineering becomes part of a controlled production workflow rather than a standalone code-generation tool.
Sophos’s experience shows that the biggest gains from AI can come from redesigning the entire delivery process. Cutting pipeline delivery from as much as 16 hours to 2–5 hours is a major result. Creating a repeatable, governed system for building production-ready data pipelines may prove even more important as Sophos prepares its data for AI applications and autonomous agents.
Want to learn the exact architectural and operational steps they took to get there? Read the full story on how Sophos achieved this transformation here.
