Senior Analytics Engineer (Semantic Layer)

Job description

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 together 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 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 WILL BE A PLUS 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 PERSONAL PROFILE Strong analytical and problem-solving mindset Ability to work independently in a fast-paced environment Detail-oriented approach to data quality and business consistency Proactive communication and collaboration skills Ownership mindset and focus on long-term scalability Join a project where data consistency, analytics scalability, and AI readiness are treated as core business priorities. We are looking for a Senior Analytics Engineer to help build an AI-first semantic layer that standardizes business metrics across dashboards, reporting systems, analytical products, and AI-driven applications. You will work closely with cross-functional stakeholders and engineering teams to transform raw data into governed business meaning that can be trusted across the organization. We at Sigma Software offer the opportunity to contribute to large-scale AdTech and analytics initiatives, work with modern data platforms, and influence the future of self-service analytics and AI-powered reporting solutions. CUSTOMER Our Customer is a leading technology company operating in the AdTech and digital monetization domain. The company develops scalable self-service advertising and analytics solutions used by enterprise clients worldwide to manage campaigns, reporting, and monetization workflows. The environment combines large-scale data processing, analytics engineering, and AI-driven innovation, with a strong focus on trusted metrics, reporting consistency, and data governance. PROJECT The project focuses on building an AI-first semantic layer that standardizes and governs business metrics across analytics platforms, dashboards, reporting systems, and AI-powered applications. The team is developing canonical analytical models to ensure that concepts such as revenue, impressions, campaigns, and advertiser activity are consistently defined and reusable across the organization. The initiative combines semantic modeling, modern data transformation practices, BI enablement, and AI/LLM-oriented data preparation. The goal is to establish a scalable analytics foundation that supports self-service analytics, trusted reporting, and future customer-facing analytical products.