Senior Analytics & Semantic Layer Engineer

Required skills: Documentation, SQL, Business Intelligence (BI), Operations, Artificial Intelligence (AI), Testing, Software as a Service (SaaS), Microsoft Platform, dashboard, transformation Project Information: Start: ASAP / Flexible Contract length: 6 months + prolongations Contract type: B2B Rate: 470-500 SEK/h Remote: 100% remote Business trips: none Location: PL About the Role A data platform isn't useful simply because pipelines run. The same concepts — revenue, impressions, delivered campaigns, active advertisers, and other business metrics — should maintain consistent meanings across various 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 a bridge between raw data and trusted business meaning. What You'll Do Design the semantic modeling approach. Build canonical analytical models on the core data platform. Define governed metrics with Finance, Product, Ad Operations, Sales, and other business owners. Translate business definitions into tested technical implementations. Build reusable semantic models for 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-standardization process between business, Data & AI, and Platform Engineering. Key Requirements Strong analytics engineering or data modeling background . Excellent SQL skills. Experience with modern transformation frameworks such as dbt or equivalent. Strong understanding of dimensional, canonical, and semantic modeling 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. Nice to Have Experience implementing semantic layers or metrics layers. SaaS or advertising technology experience. Finance/revenue reconciliation experience. Multi-tenant analytics experience. Experience preparing structured data and metadata for AI/LLM consumption. Experience with customer-facing analytics.