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 do

• Design 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 for

• Strong 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 valuable

• Experience 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 like

Within 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.