Key skills
TypeScriptC++CGoKotlinRustShellAIKafkaReactNode.jsgRPCAWSAzureGCPKubernetesPostgreSQLRedisTimescaleDBPulsarAgileScrum
About this role
Tradeify is a fintech company developing sophisticated infrastructure for real-time data analysis and decision-making. They are seeking a pragmatic engineer to design and deliver mission-critical, multi-service systems that power large-scale data processing.
Responsibilities:
- Design and maintain multi-region microservices that ingest, deduplicate, and replay futures market data streams with sub-millisecond ordering guarantees
- Build a hybrid persistence layer leveraging PostgreSQL, TimescaleDB, Redis, and ClickHouse and know when to apply each in real time
- Architect backpressure-aware pipelines across Kafka, NATS, and Pulsar simultaneously, with seamless failover strategies
- Implement an AI and anomaly detection service consuming millions of events per second without packet loss
- Deliver a multi-monitor Electron desktop shell embedding a proprietary charting platform, with hot-reloading and drag-drop layout persistence
- Collaborate in a Scrum sprint process: participate in planning, estimation, stand-ups, and retrospectives
- Write integration harnesses that simulate exchange disconnects, order fills, tick sequence gaps, and user recovery scenarios
Requirements:
- 5+ years of engineering experience across Go, Rust, Node.js, TypeScript, Kotlin, and C++, with production code in all of them
- Deep expertise in WebSockets, gRPC, FIX, and custom binary protocols
- Prior experience in high-frequency trading
- Proven ability to ship React/Electron apps with real-time state synchronization across thousands of widgets
- Experience running stateful microservices with rolling upgrades and zero downtime
- Working knowledge of Scrum/Agile, delivering increments under tight deadlines and stress
- Ability to thrive under pressure, communicate clearly, and collaborate with teammates rather than work in isolation
- Built or operated Kafka clusters moving > 10M events/sec
- Familiar with CRDTs vs Lamport clocks for ordering ticks across unreliable streams
- Implemented your own message broker or time-series database “for fun.”
- Strong awareness of WCAG AA accessibility in multi-pane trading dashboards
- Deployment experience across AWS, GCP, Azure, and bare-metal Kubernetes simultaneously