Senior Analytics Engineer (Semantic Layer)

Company DescriptionJoin 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.CUSTOMEROur 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.PROJECTThe 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.Job DescriptionDesign and implement scalable semantic modeling approaches for enterprise analyticsBuild canonical analytical models on top of the core data platformDefine and govern business metrics together with Finance, Product, Ad Operations, Sales, and other stakeholdersTranslate business definitions into robust and tested technical implementationsDevelop reusable semantic models consumable by BI tools, analytical products, and AI agentsCreate and maintain dashboards and analytical solutions for internal stakeholdersReconcile critical metrics across operational systems, reporting platforms, and financial dataImplement automated testing for metrics, transformations, and business rulesMaintain documentation, metadata, and lineage for business definitions and analytical assetsContribute to establishing company-wide data standardization processesDesign intuitive datasets optimized for analyst workflows and machine consumptionSupport the evolution of self-service analytics capabilitiesEnsure governed metric definitions are consistently used across internal and customer-facing reporting systemsQualificationsAt least 5 years of experience in Analytics Engineering or Data EngineeringStrong background in analytics engineering, data modeling, or business intelligence engineeringAdvanced SQL skillsCommercial experience with dbt or similar modern data transformation frameworksStrong understanding of dimensional, canonical, and semantic modeling conceptsExperience building production-grade BI solutions and analytical productsExperience collaborating with non-technical stakeholders to define business metrics and KPIsStrong understanding of data quality validation, testing, and reconciliation processesAbility to transform ambiguous business concepts into clear technical definitionsHands-on experience implementing semantic or metrics layersExperience in SaaS or AdTech domainsExperience working with modern cloud-based data platforms and scalable analytics architecturesAt least an Upper-Intermediate level of EnglishWILL BE A PLUSFinance and revenue reconciliation experienceExperience with multi-tenant analytics environmentsHands-on experience preparing structured data and metadata for AI/LLM consumptionExperience building customer-facing analytics and reporting solutionsAdditional InformationPERSONAL PROFILEStrong analytical and problem-solving mindsetAbility to work independently in a fast-paced environmentDetail-oriented approach to data quality and business consistencyProactive communication and collaboration skillsOwnership mindset and focus on long-term scalability