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AI Summary
Fliff is building sports gaming and entertainment products for a fast-moving, highly engaged audience. Behind every market, event, contest, player prop, and in-app experience is a data platform that needs to be accurate, reliable, and fast.
The DataFeed Team owns the systems that bring external sports data into Fliff: ingesting feeds, normalizing provider-specific formats, validating data quality, and making that data available to the rest of the platform.
\nWe are looking for a Senior Python Engineer to help us build and evolve the core systems behind Fliff’s sports data platform.
This is not a generic backend role. You will work close to the domain: sports events, leagues, teams, players, markets, odds, scores, schedules, and provider-specific edge cases. You will help make sure our data is correct, timely, observable, and resilient when external feeds behave unpredictably.
You’ll join a squad where engineering decisions have direct product impact. The systems you build will support real-time experiences across Fliff and help our teams move faster with confidence.
- Design, build, and maintain Python services for sports data ingestion, transformation, and distribution
- Integrate with third-party sports data providers and handle differences between provider models, formats, and update patterns
- Build reliable pipelines for near real-time and batch data processing
- Improve data validation, reconciliation, monitoring, alerting, and replay tooling
- Work on domain models for events, competitions, participants, markets, odds, scores, and related sports entities
- Investigate production issues, trace data problems, and improve system observability
- Collaborate with backend, product, trading, QA, and platform teams to deliver dependable data flows
- Contribute to architecture decisions, code reviews, technical standards, and mentoring within the squad
- 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
- 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
Sports data is full of real-world complexity. Providers disagree. Events change. Markets open and close. Names, IDs, schedules, scores, and statuses need to be mapped, checked, and trusted.
In the DataFeed Squad, you’ll work on systems where correctness matters, latency matters, and operational visibility matters. You’ll have room to shape architecture, improve reliability, and build tools that make the whole engineering organization more effective.
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