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Job Description
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 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.
### Nice to have
- 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).
### Your salary
- We offer a salary range of EUR 72,000 – EUR 82,000 per year.
- Final compensation will be based on experience, skills, and overall fit for the role.
### Your benefits with us
- 30 Days Annual Leave with a 5-day workweek– enjoy extra paid time off to rest, recharge, and balance your work and personal life
- Flexible Work Model – 100% remote work within Germany
- Fitness & Well-being (Choose 1 of 2): Stay active your way! Choose either our free Move Republic Program to earn points for rewards, OR join EGYM Wellpass for just a €20/month co-pay to access a massive network of gyms, pools, and studios (we cover the rest!)
- Access to Udemy – boost your skills with thousands of high-quality online courses
- Deutschland Ticket Subsidy – commute affordably – we cover part of your ticket cost
- Company Pension Scheme (bAV) – receive an above-average employer contribution in Germany or Austria
- Flexible Working Hours – adjust your schedule to fit your lifestyle
- Employee Discounts – access top deals in Germany and Austria via our benefits portal
Key Responsibilities
Build and optimize cloud-native data pipelines on AWS using ETL/ELT and infrastructure tools.
Design and evolve data models for analytics, operational use cases, and AI/ML.
Embed governance, testing, and lineage to ensure data reliability and trust.
Partner with product and engineering teams to solve ambiguous business problems.
Evaluate and evolve data mesh patterns and emerging AI tooling.
Support financial and regulatory reporting workstreams with a focus on correctness and auditability.