Senior Python Engineer, DataFeed Team

Serbia·BelgradeRemoteContractsenior

OtherPython Engineer

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Overview

Fliff is building sports gaming and entertainment products for a fast-moving, highly engaged audience. Behind every market, event, contest, player prop,

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OtherPython Engineer

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.

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.

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.