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

Sport Alliance operates the data platform behind Magicline and Finion, serving thousands of gyms and over 10 million members. The company focuses on building a data platform powering AI and analytics in the Fitness and SaaS industry.

• Minimum 3 years of data engineering experience focusing on data warehousing with readiness for increased ownership.
• Strong SQL and Python skills for data tasks.
• Comfortable working with AI tools, possessing the judgment to review and validate AI output critically.
• Solid understanding or fast learning capability of data architecture and modeling principles (dimensional modeling, slowly-changing dimensions, incremental patterns).
• Hands-on experience with dbt on cloud warehouses.
• Working knowledge of relational databases and exposure to non-relational databases like DynamoDB and MongoDB.
• Proven ability to debug complex data issues and implement durable fixes.
• Excellent written and verbal English skills.

Nice to have:

• Experience with AWS services such as Redshift, EMR, Glue, S3, IAM; experience with GCP or Azure is also welcomed.
• Familiarity with streaming/CDC technologies like Kafka, Kinesis, or Debezium, or knowledge of data mesh.
• Experience building data pipelines for ML or AI systems.
• Exposure to financial, payments, or regulated reporting data.
• German language skills (helpful but not required).

• Build and optimize cloud-native data pipelines on AWS using AI tooling, including ETL/ELT and infrastructure components like Aurora, Redshift, dbt, Spark/EMR, Airflow, and CDC.
• Design and evolve data models powering analytics, operational use cases, and AI/ML across thousands of studios.
• Enhance data reliability by embedding governance, testing, and lineage.
• Collaborate with product and engineering teams to transform ambiguous business problems into robust data solutions.
• Evaluate and adopt new data approaches, including data mesh patterns and emerging AI tooling.
• Develop expertise in financial and regulatory reporting workstreams where correctness and auditability are essential.