Senior Python Engineer, DataFeed Team

Job description

Fliff is the arena for high-upside play. We are dismantling the paywalls of legacy sportsbooks and reimagining the economics of social games to pioneer the next generation of rewards-driven entertainment spanning sports, casino, predictions, collectibles, and beyond. Powered by a frictionless freemium architecture, Fliff democratizes access and gives millions of players nationwide a real stake, real thrill, and real upside. By combining authentic competitive drive with instant rewards and interactive games, we are turning passive sports fans and casual gamers into active, empowered participants – unlocking the player in everyone. 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.