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.
About The Role
We 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.
What You’ll Do
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
What We're Looking For
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
Nice To Have
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
What Makes This Role Interesting
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.
