Betting Platform

Project Overview

As part of a distributed engineering team, I contribute to the development of a large-scale sports betting platform responsible for processing live sporting events, market odds, and betting data in real time.

The platform operates on an event-driven architecture that ingests live sports feeds, processes market updates, and distributes betting data to multiple downstream consumer applications with strict requirements for latency, availability, and scalability.

My work spans backend services, streaming infrastructure, cloud-native deployments, CI/CD automation, and frontend platform integrations, with a strong focus on reliability and developer productivity.

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        Live Sports Feeds
                │
                ▼
        Kafka Topics
                │
         ┌──────┼──────┐
         ▼      ▼      ▼
        Odds  Markets  Results
        Service Service Service
                │
                ▼
        MySQL / Cache
                │
         ┌──────┼──────┐
         ▼      ▼      ▼
        REST   Kafka  GraphQL
                │
                ▼
        Betting Platforms

Key Features

Real-Time Odds Processing

Processes live sporting events and continuously updates betting markets as events unfold.

Event-Driven Architecture

Leverages Kafka-based streaming pipelines to distribute market data and betting events across multiple services and consumer applications.

Multi-Platform Data Distribution

Exposes betting and market information through REST APIs, event streams, and frontend applications used by multiple betting products.

Cloud-Native Infrastructure

Runs on Kubernetes-based infrastructure with automated deployment, scaling, and monitoring capabilities.

Scalable Frontend Platform

Supports React and Angular micro-frontends that consume real-time market data and betting services.

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My Contributions

• Designed and implemented Kafka-based event processing pipelines for live sports events and market odds.
• Developed backend services responsible for ingesting, transforming, and distributing betting data.
• Built REST APIs and event-driven interfaces consumed by multiple downstream applications.
• Designed persistence layers for market and odds data using MySQL and cloud-native storage services.
• Led CI/CD modernization initiatives across backend, frontend, and infrastructure repositories.
• Automated build, testing, deployment, and release workflows for Java, Node.js, React, and Angular applications.
• Contributed to cloud-native platform architecture running on Kubernetes.
• Supported micro-frontend initiatives to improve frontend scalability and independent deployments.
• Collaborated with product, platform, and infrastructure teams to improve operational reliability and development velocity.

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Technical Highlights

High-Throughput Streaming Architecture

Built Kafka-based pipelines capable of processing continuous streams of sporting events and betting market updates with low latency and high reliability.

Cloud-Native Platform Engineering

Developed and deployed containerized services on Kubernetes infrastructure using Infrastructure as Code and automated deployment pipelines.

Multi-Consumer Event Distribution

Designed services capable of distributing betting data through both synchronous APIs and asynchronous event streams, enabling flexible integration patterns.

Platform Automation

Standardized CI/CD workflows across multiple technology stacks, reducing deployment complexity and improving release consistency.

Microservices Architecture

Developed independently deployable services that could scale based on event volume while maintaining system resilience and fault isolation.

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Challenges & Solutions

Challenge

Live sports betting platforms require extremely fast processing of continuously changing market data while maintaining reliability, consistency, and scalability during major sporting events with unpredictable traffic spikes.

Solution

Implemented an event-driven architecture built around Kafka, containerized microservices, automated deployments, and scalable cloud infrastructure to ensure low-latency data distribution and operational resilience.

Outcome

Successfully delivered backend services and platform capabilities that support real-time market processing, scalable consumer integrations, and reliable deployment workflows across a distributed system.

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Technology Stack

Backend Scala, Quarkus, Hibernate ORM, Node.js, Express

Streaming & Messaging Apache Kafka, Event-Driven Architecture

Databases MySQL, DynamoDB, RDS

Cloud AWS Lambda, EKS, SQS, SNS

Infrastructure Docker, Kubernetes, Terraform, Helm

Frontend React, Angular, Micro-Frontends

APIs REST, GraphQL

DevOps GitHub Actions, Jenkins, CI/CD Automation

Domain Real-Time Data Processing, Distributed Systems, Event Streaming, Sports Technology