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You will own the end-to-end data journey from ingestion to business impact, designing scalable models that support both human and AI-driven analytics. Additionally, you will collaborate with cross-functional teams to turn business requirements into maintainable data products while ensuring high data quality and governance.
Join Sport Alliance and shape the data foundation behind Magicline and Finion, used by thousands of fitness businesses and more than 10 million members. You'll own the modelling layer that powers analytics, operations and AI across the business.
You'll join an AI-first data team where LLMs and internal tools are part of how we work. Whether you're an experienced Analytics or Data Engineer or a strong mid-level engineer ready for more ownership, we'd love to hear from you.
\nOwn the full journey from raw data to business impact: from ingestion and dbt models on AWS to the insights, metrics and AI use cases that run on top of them across thousands of studios.
Design clean, scalable and reliable data models that both people and AI systems can confidently build on.
Make data quality part of the architecture through testing, documentation, lineage and governance.
Partner with Data Engineering on ingestion and pipelines to ensure your models are built on fresh, dependable data.
Work closely with Product, Engineering and Finance to turn ambiguous business questions into useful, maintainable data products.
Build with AI at the core: use AI-assisted tooling in your daily workflow, and make our data AI-ready (clear semantics, documentation, lineage) so agents and ML models can use it reliably.
Grow into financial reporting for Finion Capital, working alongside experienced finance specialists.
3+ years of experience in Analytics Engineering, Data Engineering or a similar role, ideally with a strong focus on data warehousing.
Strong SQL skills and hands-on experience with dbt and a cloud data warehouse.
A solid understanding of data modelling concepts such as dimensional modelling, slowly changing dimensions and incremental patterns — or the motivation to deepen that expertise quickly.
Practical Python skills for data work.
Confidence working with AI-assisted tools, paired with the judgment to review, validate and improve their output.
A systematic approach to debugging complex data issues and solving them at the root cause.
English at C1 level or above, both written and spoken.
Experience with financial, payments or regulated reporting data.
German language skills.
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