About the role
Dexory is building its first company-wide data capability, and this is its founding hire. You will build and own the data foundation that brings our various sources together, model them into clean and trustworthy tables, and define the metrics and business concepts so that how we are doing means the same thing to everyone, and so that AI can query our data safely. The first and most important outcome is a reliable, queryable view of customer and site performance against contracted SLAs and KPIs. From that same foundation, you will go on to serve the rest of the business: deployment and billing readiness, finance, product usage and operational efficiency. This is a senior, hands-on role for someone who is data- and AI-automation savvy, can make the big architectural calls and still ship, and can navigate the genuinely hard part: getting the business to agree on what a site, a customer, or a deployment actually is. You will be a team of one to begin with, setting the standards, proving value quickly with early wins, and shaping the function and team we build around you, with a clear path to lead it. This will involve a minimum of 2 days a week onsite at our Wallingford facility.
Responsibilities: Build the data foundation, bringing operational, product, commercial and contractual data into a single queryable layer, and decide the architecture and system of record for our core concepts: customer, site, deployment and contract Embed Dexory’s data security
requirements into all foundational architecture and engineering work, ensuring security is a core design principle rather than an afterthought. Make customer and site performance answerable on demand by modelling perception accuracy, scan and mission performance, obstructions and scan coverage, connectivity and other operational signals against the SLAs and KPIs we have committed to per site. Define the metrics and business definitions with each team so that key terms are consistent and the data is trustworthy. Establish the metrics bible that controls the lens through which AI and reporting interpret our data. You facilitate and codify these definitions rather than owning every one alone. Use AI automation to join the dots between systems and to make data genuinely self-serve: connectors and pipelines where needed, and a trustworthy natural-language and query layer so teams get correct, context-aware answers instead of raw-data guesses. Partner with the engineering teams, platform, autonomy and perception, to have them expose source data cleanly, rather than building every pipeline yourself. Your focus is on what the data means and how it fits together. Own the ongoing health of the foundation: monitor pipelines, fix what breaks when sources change, and protect the trust that makes this the company's source of truth. Make pragmatic build-versus-buy decisions, set the engineering standards for the future team, and deliver early, visible wins within the first few months. Help define, recruit and mentor