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

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 boundaries by avoiding unstructured bespoke consulting. 
• Build repeatable methods and standards rather than one-off customer-specific solutions. 

6. Support future product modules and AI-enabled analytics

• Support future product surfaces around AI-assisted operational workflows. 
• Provide the semantic and analytical structures needed for AI-assisted explanation, reporting, recommendations and user-facing analytics. 
• Use AI tools to accelerate documentation, SQL/DAX scaffolding, semantic-model review, data-quality analysis and report prototyping. 
• Ensure AI-generated analytics remain grounded in governed models and reviewed logic. 

7. Engage with business stakeholders without becoming a request queue

• Gather and clarify business requirements directly from stakeholders. 
• Translate business questions into reusable data-model, semantic-model or reporting needs. 
• Push back constructively when requests are unclear or better solved through existing models. 
• Explain data-model decisions in business language. 
• Clarify what can be self-served, what needs governed modeling, and what should become a product or platform capability.

]]> Required Experience

• Senior-level experience in analytics engineering, semantic modeling, BI engineering, data modeling or a closely related role. 
• Strong practical knowledge of dimensional modeling, star schemas, snowflake schemas and analytical model design. 
• Hands-on experience with Microsoft Fabric or the Azure data stack, with a clear willingness and ability to work deeply in Fabric. 
• Strong Power BI semantic-model experience, including measures, model relationships, performance considerations and governance. 
• Strong SQL and data-warehouse / lakehouse understanding. 
• Experience translating business requirements into scalable analytical structures. 
• Ability to communicate clearly with both technical and non-technical stakeholders. 
• Strong data-quality mindset: validation, reconciliation, lineage, consistency and trust. 
• Ability to work autonomously while collaborating closely with Data, Product, Engineering, Customer Operations and Commercial teams. 

Required Working Style

• You think in models, not only reports. 
• You prefer reusable data assets over one-off dashboards. 
• You can challenge business requests without blocking business outcomes. 
• You document and govern what you build. 
• You are comfortable working in a company that is changing its operating model. 
• You use AI to accelerate work, but you do not treat AI output as automatically correct. 
• You can operate in ambiguity, but you do not leave ambiguity undocumented. 

Strong Plus

• Experience in renewable energy, energy markets, asset management, infrastructure, utilities or industrial analytics. 
• Experience with SCADA, telemetry, asset-performance data, energy-settlement data or operational event data. 
• Experience with Microsoft Fabric certification paths or Microsoft data-platform governance. 
• Experience designing KPI dictionaries, semantic-layer governance or enterprise BI standards. 
• Experience using AI assistants or copilots for analytics engineering, documentation, SQL/DAX support, model review or reporting acceleration. 
• Familiarity with data-product thinking. 
• Familiarity with data governance, data contracts, CI/CD for analytics, or semantic-model lifecycle management. 

AI Expectations

This role does not require deep machine-learning research expertise. It does require an AI-enabled working style. The successful candidate should be able to use AI tools to:

• accelerate SQL and DAX drafting; 
• document models and measures; 
• summarize data-model logic for business users; 
• generate first-pass validation checklists; 
• scaffold report concepts; 
• identify potential metric inconsistencies;