Senior Python Engineer, DataFeed Team
On-siteBerlin, State of Berlin, Germany
Job Summary
Design, build, and maintain Python services for sports data ingestion, transformation, and distribution by integrating with third-party providers and handling diverse update patterns. Construct reliable pipelines for near real-time and batch processing while improving validation, reconciliation, monitoring, and alerting tooling. Work on domain models for events, odds, and scores to ensure data accuracy and resilience when external feeds behave unpredictably. Investigate production issues, trace data problems, and collaborate with product and platform teams to deliver dependable data flows. Participate in on-call rotations and contribute to architecture decisions and code reviews within the squad.
Required Qualifications
- 5+ years of strong production experience with Python
- Experience designing and operating backend services in production
- Solid Django experience (not necessarily expert level), with asynchronous programming skills (asyncio)
- Production experience with Apache Kafka
- Solid understanding of APIs, distributed systems, async processing, and data pipelines
- Strong SQL skills and experience with relational databases, especially PostgreSQL
- Experience integrating with external APIs, feeds, or third-party data providers
- Ability to reason carefully about data correctness, edge cases, and failure modes
- Experience with monitoring, logging, alerting, and debugging production systems
- A senior ownership mindset: you can break down ambiguous problems, make pragmatic technical decisions, and communicate clearly
- Strong problem-solving skills and comfort doing code reviews
- Willingness to participate in on-call rotations
Desired Qualifications
- Experience in sports betting, gaming, fantasy sports, sports data, fintech, trading, or other real-time data domains
- Experience with Django, Kafka, Redis, PostgreSQL, or similar technologies
- Experience with Go / Golang, especially for high-throughput backend services, data processing, or performance-sensitive systems
- Experience with event-driven architecture or message queues
- Experience building data validation, reconciliation, or replay systems
- Cloud, Docker, Kubernetes, or infrastructure-as-code experience
- Interest in using AI-assisted engineering tools thoughtfully to improve development speed and quality
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