Sport Alliance GmbH · Remote (Germany)
Skills: Python, SQL, MongoDB, AWS
Join Sport Alliance and help build the data platform behind Magicline and Finion, serving thousands of gyms and 10M+ members. You'll tackle real engineering challenges, from high-volume data modeling and cost-efficient warehousing to freshness trade-offs and financial data that has to be exactly right.
We're building an AI-first data team. We use cutting-edge LLMs and internal tools to work faster, and we expect you to use and improve them. The platform you build powers our AI and analytics, making clean, reliable, well-modeled data essential.
Whether you're an experienced engineer ready to own the platform or a strong mid-level engineer eager to grow into the role, we'd love to hear from you.
Your position in our team - Build and optimize cloud-native data pipelines on AWS — ETL/ELT and the infrastructure underneath (Aurora, Redshift, dbt, Spark/EMR, Airflow, CDC) — using AI tooling as a standard part of the workflow. - Design and evolve the data models that power analytics, operational use cases, and AI/ML across thousands of studios. - Help raise the bar on data reliability — embed governance, testing, and lineage so both people and AI systems can trust the numbers by default. - Partner with product, and other engineering teams to turn ambiguous business problems into robust data solutions. - Evaluate and evolve new approaches with us — data mesh patterns, and emerging AI tooling — and help decide what genuinely earns a place in our stack. - Grow into our financial and regulatory reporting workstream (Finion Capital), where correctness and auditability matter most.
Your profile - 3+ years in data engineering with a focus on data warehousing — and the appetite to take on more ownership than you've held so far. - Strong SQL and Python for data work. - Working with AI tools feels natural to you — and, just as important, the judgment to review and validate what they produce. You treat AI output as a draft to verify, not an answer to trust, especially where …truncated… le…
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