Senior Analytics Engineer / Semantic Model Owner (Microsoft Fabric / Power BI)

About the role

We are looking for a Senior Analytics Engineer / Semantic Model Owner (Microsoft Fabric / Power BI) to help build and govern QBi's next-generation analytics foundation. This is not a report-factory role. The role exists to reduce one-off reporting effort by building reusable, governed and scalable data products : Microsoft Fabric data structures, Power BI semantic models, KPI definitions, validation rules, reporting patterns and renewable-domain data models that can be reused across customers, internal teams and future product modules. The ideal candidate combines strong data modeling and Microsoft Fabric capability with data-quality discipline and the ability to translate industry-standard workflows into robust analytical structures . This person should be comfortable working with business stakeholders, product teams, data engineers, analysts and leadership. They must be able to distinguish between a one-off dashboard request and a recurring data-model need that should become part of QBi's reusable data foundation. Mission Your mission will be to turn QBi's renewable-energy knowledge into scalable data and semantic-model assets. You will be instrumental in showing how and helping QBi move from fragmented reporting and ad hoc BI execution toward a governed analytics layer that supports: Existing customer reporting and reporting modernization; Microsoft Fabric / Power BI semantic-model governance; Technical Analytics and data-quality logic; Data Model-as-Infrastructure / Renewable Data Foundation offers; Future AI-native product and operational workflows; Future Revenue Copilot and hybrid revenue intelligence products. What This Role Is - and Is Not This role IS a senior analytics engineering role; a semantic model ownership role; a Microsoft Fabric / Power BI governance role; a renewable-domain data-model role; a bridge between business needs, data structures and scalable analytics; a role that uses AI to accelerate analytics engineering, documentation and model review. This role IS NOT a generic Power BI report-builder role; an open-ended "stakeholder asks, we build a dashboard" role; a data-science key trends informed role; a deep ML engineering role; a generic Microsoft consulting role; a role that accepts unbounded custom work without converting it into reusable patterns. Key Interfaces This role will work closely with: Data Engineering; BI / Reporting; Product Builders; Architecture / Platform; selected external Fabric or data-platform specialists where needed. And from time to time with: Customer Operations and Professional Services; Commercial / KAM teams; future Revenue Copilot and Renewable Data Foundation owners; Key Responsibilities 1. Own and evolve QBi's semantic model layer Design, maintain and improve reusable semantic models for Power BI and Microsoft Fabric. Define consistent KPI logic, measures, dimensions, hierarchies and analytical relationships. Maintain metric definitions, calculation logic and semantic-model documentation. Ensure business users, analysts and AI tools work from trusted semantic foundations. Review changes to key measures, shared datasets and customer-facing analytical structures. 2. Build scalable data models in Microsoft Fabric Design conceptual, logical and physical data models for renewable-energy use cases. Work with Microsoft Fabric Lakehouse, Warehouse, semantic models and related data-engineering patterns. Translate renewable asset, portfolio, contract, event, revenue, reporting and data-quality concepts into scalable model structures. Collaborate with data engineers on ingestion, transformation and validation patterns. Support reusable Fabric architectures for internal and customer-facing use cases. 3. Reduce custom BI workload through reusable analytics assets Convert recurring reporting needs into reusable semantic models, templates, report families and governed data products. Challenge ad hoc report requests when the better answer is model improvement, template creation or self-service enablement. Build reporting structures that reduce manual BI customization. Help define which requests become product features, governed analytics patterns or bounded expert-service work. 4. Strengthen data quality, governance and trust Define validation rules, reconciliation checks, data-quality indicators and confidence signals. Classify and explain data-quality issues in operational and customer-facing contexts. Support lineage, data ownership, metric governance and semantic-model change control. Identify where data-quality limitations affect reporting, customer trust or product behavior. Ensure analytical outputs are explainable and defensible. 5. Support QBi's Renewable Data Foundation strategy Turn QBi's renewable-data knowledge into reusable data-model assets. Create renewable-domain object dictionaries, data-domain maps, semantic patterns and implementation templates. Contribute to Microsoft Fabric-based customer enablement offers. Protect QBi's model quality and IP bo

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