About the Company

Nordic Tyre Group is the largest tyre wholesaler in the Nordic countries and the Baltics. We use the power of technology to connect leading retailers and their customers with the world's best tyres. We offer products for passenger cars, trucks, construction and agricultural machinery. Learn more about the company at nordictyregroup.com.

We are currently present in six markets: Norway, Sweden, Finland, Estonia, Latvia and Lithuania. The group has about 500 employees and a turnover of 4,3 mdkr.

Join our team of great talents!

Why this role exists

Nordic Tyre Group is transforming a decades-long, successful tyre wholesaling business into a modern, data-driven one. We believe our future is in data, and we have ambitious growth goals that depend on it.

Over the past two years we have standardised finance across all business units on Microsoft Business Central, rebuilt our B2B portal, and put demand forecasting, churn, cross-sell and segmentation models into the business. The foundations are in place. What we need now is someone to turn that foundation into a coherent analytics capability — and to make the rest of the group fluent in it.

We are looking for a curious-minded Analytics Engineer to build, maintain and optimise our analytics stack, which runs on Microsoft Fabric and is built on dbt.

The hands-on engineering is half the job. The other half is the analytics framework itself: the metric definitions, modelling standards and ways of working that let the group ask better questions and trust the answers. This is a role for someone who is as interested in what the numbers mean as in how they are produced.

Analytics engineering

– Build and maintain modular, well-tested dbt models on Microsoft Fabric.

– Optimise existing transformations for performance, cost and maintainability.

– Own data models end to end — from source through to the semantic layer.

Build the analytics framework

– Define and document the metrics that matter across the group, so that one metric has one definition everywhere.

– Set modelling standards, naming conventions and testing practice — and hold the line on them.

– Build reusable structures so that new questions do not each require a new pipeline.

Turn data into decisions

– Develop Power BI datasets and reports that stakeholders genuinely use.

– Go past reporting into analysis: identify what the numbers mean and what should change as a result.

– Work directly with commercial, supply chain and finance teams to frame the questions worth answering.

Bring people with you

– Explain analytical work to non-technical stakeholders clearly and without condescension.

– Raise data literacy across the group and make self-service genuinely usable rather than nominally available.

Protect the foundation

– Build testing, monitoring and documentation into the pipeline as standard.

– Treat data quality as a feature of the product, not a cleanup task.

Levels below describe where we need you on day one, not where we expect you to stay.

SQL - Extremely competent

The core of the role. Complex modelling, performance tuning, and comfort in a warehouse of meaningful size.

dbt - Competent

Our transformation layer. You should be at home with models, tests, macros, and version-controlled analytics.

Power BI - Competent

Our reporting layer. Semantic models and reports that stakeholders trust and actually open.

Python - Familiar

Enough to automate, glue, and prototype. You are not expected to be a software engineer.

Microsoft Fabric - Advantageous

Where our stack runs. Direct experience helps, but a strong candidate from an equivalent stack will not be ruled out.

Two qualities matter more to us than any single technology on the list above.

– Analytics. A genuine instinct for what a number is telling you, what it is hiding, and which question to ask next.

– Pedagogy. The ability to teach — to make an analytical result land with a stakeholder who has no interest in how it was built.

Alongside those: curiosity, comfort with the ambiguity of a business mid-transformation, and the judgement to know when a pragmatic answer today beats a perfect one next month.

– Experience building analytics metrics that were adopted and used, not just delivered.

– A track record of adding value through insight, communicated pedagogically to key stakeholders.

– Comfort working across multiple markets, entities and source systems.

– Wholesale, distribution, retail or logistics experience is an advantage, though not a requirement.

– Location: Riga or Stockholm.

– Team: joins the group data and analytics team, working alongside our data scientist and data engineer.

– Language: English is the working language of the group.

– An agreed, documented metric framework in active use across the business units.

– A dbt project with real test coverage, clear ownership and no undocumented corners.

– Stakeholders answering their own routine questions — and bringing you the harder ones.