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

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.

• 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.

• 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 correctness is non-negotiable.
• A solid grasp of data architecture and modeling principles (dimensional modeling, slowly-changing dimensions, incremental patterns) — or the drive to deepen it fast.
• Hands-on experience with dbt on a cloud warehouse — this is where you'll live day to day.
• Working knowledge of relational and some exposure to non-relational Databases (DynamoDB, MongoDB).
• A track record of debugging tricky data issues and shipping durable fixes.
• Excellent written and verbal English.

• AWS experience (Redshift, EMR, Glue, S3, IAM); experience with GCP or Azure is also welcome.
• Streaming/CDC technologies (Kafka, Kinesis, Debezium) or data mesh.
• Experience building data pipelines for ML or AI systems.
• Exposure to financial, payments, or regulated reporting data.
• German (helpful for some stakeholder work, but not required).