Senior Analytics Engineer (Semantic Layer)

Join a project where data consistency, analytics scalability, and AI readiness are treated as core business priorities. Work closely with cross-functional stakeholders and engineering teams to transform raw data into governed business meaning that can be trusted across the organization. Contribute to large-scale AdTech and analytics initiatives and influence the future of self-service analytics and AI-powered reporting solutions. The project focuses on building an AI-first semantic layer to standardize and govern business metrics across analytics platforms, dashboards, reporting systems, and AI-powered applications, ensuring consistent definitions and reusability across the organization.

• At least 5 years of experience in Analytics Engineering or Data Engineering
• Strong background in analytics engineering, data modeling, or business intelligence engineering
• Advanced SQL skills
• Commercial experience with dbt or similar modern data transformation frameworks
• Strong understanding of dimensional, canonical, and semantic modeling concepts
• Experience building production-grade BI solutions and analytical products
• Experience collaborating with non-technical stakeholders to define business metrics and KPIs
• Strong understanding of data quality validation, testing, and reconciliation processes
• Ability to transform ambiguous business concepts into clear technical definitions
• Hands-on experience implementing semantic or metrics layers
• Experience in SaaS or AdTech domains
• Experience working with modern cloud-based data platforms and scalable analytics architectures
• At least an Upper-Intermediate level of English

Nice to have:

• Finance and revenue reconciliation experience
• Experience with multi-tenant analytics environments
• Hands-on experience preparing structured data and metadata for AI/LLM consumption
• Experience building customer-facing analytics and reporting solutions

• Design and implement scalable semantic modeling approaches for enterprise analytics
• Build canonical analytical models on top of the core data platform
• Define and govern business metrics jointly with Finance, Product, Ad Operations, Sales, and other stakeholders
• Translate business definitions into robust and tested technical implementations
• Develop reusable semantic models consumable by BI tools, analytical products, and AI agents
• Create and maintain dashboards and analytical solutions for internal stakeholders
• Reconcile critical metrics across operational systems, reporting platforms, and financial data
• Implement automated testing for metrics, transformations, and business rules
• Maintain documentation, metadata, and lineage for business definitions and analytical assets
• Contribute to establishing company-wide data standardization processes
• Design intuitive datasets optimized for analyst workflows and machine consumption
• Support the evolution of self-service analytics capabilities
• Ensure governed metric definitions are consistently used across internal and customer-facing reporting systems