AI-First Data Engineer (m/f/d)

<p><strong>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.</strong></p> <p><strong>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.</strong></p> <p><strong>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.</strong></p><br><strong>Your position in our team</strong><br> <li>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.</li> <li>Design and evolve the data models that power analytics, operational use cases, and AI/ML across thousands of studios.</li> <li>Help raise the bar on data reliability — embed governance, testing, and lineage so both people and AI systems can trust the numbers by default.</li> <li>Partner with product, and other engineering teams to turn ambiguous business problems into robust data solutions.</li> <li>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.</li> <li>Grow into our financial and regulatory reporting workstream (Finion Capital), where correctness and auditability matter most.</li> <br><strong>Your profile</strong><br> <li><strong>3+ years</strong> in data engineering with a focus on data warehousing — and the appetite to take on more ownership than you've held so far.</li> <li>Strong SQL and Python for data work.</li> <li>Working <em>with</em> AI tools feels natural to you — and, just as important, the judgment to <strong>review and validate</strong> what they produce. You treat AI output as a draft to verify, not an answer to trust, especially where correctness is non-negotiable.</li> <li>A solid grasp of data architecture and modeling principles (dimensional modeling, slowly-changing dimensions, incremental patterns) — or the drive to deepen it fast.</li> <li>Hands-on experience with <strong>dbt on a cloud warehouse </strong>— this is where you'll live day to day.</li> <li>Working knowledge of relational and some exposure to non-relational Databases (DynamoDB, MongoDB).</li> <li>A track record of debugging tricky data issues and shipping durable fixes.</li> <li>Excellent written and verbal English.</li> <br><strong>Nice to have</strong><br> <li>AWS experience (Redshift, EMR, Glue, S3, IAM); experience with GCP or Azure is also welcome.</li> <li>Streaming/CDC technologies (Kafka, Kinesis, Debezium) or data mesh.</li> <li>Experience building data pipelines for ML or AI systems.</li> <li>Exposure to financial, payments, or regulated reporting data.</li> <li>German (helpful for some stakeholder work, but not required).</li> <br><strong>Your salary</strong><br><div> <li>We offer a salary range of EUR 72,000 – EUR 82,000 per year.</li> <li>Final compensation will be based on experience, skills, and overall fit for the role.</li> </div><br><strong>Your benefits with us</strong><br> <li><strong>30 Days