Our governance practices exist today, but they grew up alongside the product and are largely home-grown: definitions live in people’s heads, ownership is informal, and data quality gets managed reactively, one escalation at a time. The Data Governance Lead will replace that with something deliberate. The priorities are named ownership and stewardship across our data domains, a correct and maintained data dictionary, a data quality framework with real metrics behind it, and disciplined compliance with the terms under which we receive client and third-party data. Cataloging, lineage, classification, and access controls follow from there.
We are building AI-powered products on top of that data, and that is what makes this role urgent. An AI product is only as trustworthy as the definitions, ownership, and quality controls underneath it. A model cannot reason correctly about a field nobody has defined, from a source nobody owns, at a quality level nobody measures. Governance is the constraint on how confidently and how quickly we can scale our AI and data products, and this role exists to remove it.
This is a builder’s role and a relentless one. The pipeline build sits with Data Engineering and dataset definition with our Data Product Manager; you will work with them, with our teams of Data Analysts, and with Data Operations, Product, Commercial, and Legal to define what good looks like, get it instrumented, and hold the organization to it. It starts as an individual contributor role, with a team to be built as the function earns it.