Senior Analytics & Semantic Layer Engineer

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.