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

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 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.

Your profile

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;
• support internal knowledge-base creation.

However, the candidate must also understand that AI sits above governed semantic models. It must not replace metric governance, data validation, security review or human judgment.

Renewable Domain Learning Expectations

Prior renewable-energy experience is valuable but not mandatory if the candidate shows strong abstraction ability and learning discipline.

What ‘good’ looks like

Success in the First 90 Days

By the end of the first 90 days, the successful candidate should have:

• mapped QBi’s current Fabric / Power BI / semantic-model maturity;
• identified the most important duplicated or fragile reporting structures;
• produced a first semantic-model governance baseline;
• documented the key reusable data domains and priority KPI families;
• proposed a target Fabric / semantic-model architecture for the highest-priority use cases;
• defined a practical backlog for reporting modernization and model consolidation;
• reduced at least one recurring reporting pain point through a reusable model or template;
• created clear rules for what should be built as custom BI versus governed reusable analytics;
• established a working relationship with Product, Data, Customer Operations and Commercial stakeholders.

Success After 6–12 Months

Success after 6–12 months means:

• QBi has a clearer, governed semantic-model backbone;
• Power BI and Fabric work is less dependent on scattered individual knowledge;
• recurring reporting requests are increasingly served through reusable models and templates;
• data-quality and metric-definition issues are more visible and better controlled;
• the company has stronger foundations for Data model-as-Infrastructure offers;
• future product modules can rely on trusted analytical structures;
• AI-assisted analytics work is faster, but still governed and credible.

Are YOU what WE are looking for?

The right candidate is not simply someone who can build dashboards.

We are looking for someone who sees data modeling as a way to create leverage: someone who can take a complex business problem, uncover the structure behind it, and turn it into a trusted analytical foundation that can be reused across teams, customers and products.

This person should combine technical depth with business judgment. They must be able to design robust semantic models, define governance logic, document decisions clearly, and challenge requests when a one-off report is not the right answer.

The ideal candidate will be senior enough to push back, structured enough to create order, technical enough to build, and business-facing enough to explain why good data models matter. They should want to help QBi move from fragmented reporting to a scalable, governed analytics foundation for the next stage of the company.

Are WE what YOU are looking for?

If you want a role where data modeling is treated as a strategic capability, not a reporting support function, QBi may be the right environment for you.

This is a transformation role. We are bringing in someone who has already mastered semantic modeling, Microsoft Fabric, Power BI governance and scalable analytics foundations, and who can help guide QBi in how to do this properly.

You will help shape the analytical backbone of the next stage of the company: trusted data models, reusable semantic layers, data-quality standards, AI-enabled analytics and renewable-energy domain structures that support products, customers and internal operations.

This is not a role built around maintaining dashboards. It is a role for someone who wants ownership, influence and impact. You will work closely with Product, Data, Engineering, Customer Operations and Commercial teams, helping move QBi from fragmented reporting toward a governed, scalable and AI-ready analytics foundation.

If you want your work to define how a company transforms its data capability, this is the kind of role where that can happen.

Why us?

What we offer

✅Competitive fixed and variable salary in line with the sector, based on merit and experience.

✅Flexible remuneration (meal vouchers, transportation vouchers, and childcare vouchers). This allows you to optimize your salary thanks to the tax savings it generates.

✅Opportunity for growth in one of the most dynamic, future-proof, and fastest-growing areas: digitalization in the renewable energy business.

✅Work with an incredible team that has created a market-leading product for large companies in the sector.

⭐Lots of creative freedom.

✅Fun is part of our day: we love organizing fun initiatives that help create an even more pleasant internal atmosphere :).

✅Flexible start time with a hybrid model (4 days of home office and 1 day of office work).

✅A prime office location in downtown Madrid.

✅Intensive working time during summer months.

✅Individual English classes (1 hour a week).