Senior Analytics Engineer

Your mission

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 t