Senior Analytics & Semantic Layer Engineer

A data platform isn't useful simply because pipelines run.DanAds wants the same concepts — revenue, impressions, delivered campaigns, active advertisers and other business metrics — to mean the same thing in product interfaces, dashboards, customer reports, AI agents and management reporting.We are therefore building an AI-first semantic layer that will provide governed metrics and definitions for both people and machines.We are looking for a Senior Analytics & Semantic Layer Engineer to build that bridge between raw data and trusted business meaning.What you'll doDesign DanAds' semantic modelling approach.Build canonical analytical models on top of the core data platform.Define governed metrics together with Finance, Product, Ad Operations, Sales and other business owners.Translate business definitions into tested technical implementations.Build reusable semantic models that support both BI tools and AI agents.Develop and maintain dashboards and analytical products for internal teams.Reconcile critical metrics across operational systems, reporting and finance.Build automated tests for metrics and business rules.Establish documentation, metadata and lineage around business definitions.Help establish the data-standardisation process between business, Data & AI and Platform Engineering.Design datasets that are intuitive for both analysts and machine consumption.Enable increasingly self-service analytics while still supporting important dashboard/reporting needs.Help ensure future customer-facing reporting uses the same governed definitions as internal systems.What we're looking forStrong analytics engineering or data modelling background.Excellent SQL.Experience with modern transformation frameworks such as dbt or equivalent.Strong understanding of dimensional, canonical and semantic modelling approaches.Experience building production BI and analytical products.Experience defining metrics jointly with non-technical stakeholders.Strong understanding of data testing and reconciliation.Ability to translate ambiguous business concepts into precise definitions.Excellent communication skills.Particularly valuableExperience implementing semantic layers or metrics layers.SaaS or advertising technology experience.Finance/revenue reconciliation experience.Multi-tenant analytics.Experience preparing structured data and metadata for AI/LLM consumption.Experience with customer-facing analytics.What success looks likeWithin six months:Corebusiness entities and metrics have canonical definitions.Important dashboards use governed data rather than duplicated business logic.The same metric produces the same answer across relevant systems.The semantic layer canbe consumed by both BI applications and AI agents.Business stakeholders understand who owns metric meaning and how changes are approved.