AITech Interview: Building Trusted DataOps Methodology
This interview was originally published on dataops.live and AITech Park.
In this interview with AITech Park, I discuss the journey from running a systems integration firm in England to co-founding DataOps.live — and the gap in the market that made it necessary.
The core insight was simple: while software development had DevOps — with its mature practices around CI/CD, testing, deployment, and monitoring — data engineering had nothing equivalent. Teams were building increasingly complex data applications with ad-hoc processes, manual deployments, and minimal automation. The result was slow delivery, fragile pipelines, and a widening gap between what data teams promised and what they could reliably deliver.
That realization led to the creation of the TrueDataOps movement and the formalization of the 7 Pillars of DataOps — a framework for bringing engineering discipline to data work. We published DataOps for Dummies to make these ideas accessible to a broader audience, and built the DataOps.live platform to make them practical.
The interview covers the philosophy behind treating data pipelines with the same rigor as application code, the importance of industry standards, and how Snowflake-powered automation enables trusted AI data products at enterprise scale.