Backend/Systems Engineer (Real-Time System)

3 мес. назад

Hong KongWorldwideOnsite

real-time systemsdata infrastructuresystem architecturemonitoring

Responsible for owning and optimizing real-time data infrastructure, focusing on integration, reliability, and data lifecycle in a high-availability system.

О компании

Our client is seeking a Backend/Systems Engineer with a deep appreciation for high-availability systems to own and optimize our real-time data infrastructure. You will own the integration, reliability, and data lifecycle of our real-time data ecosystem. You will be adding critical pieces of making our real-time data system more robust and more performant. You will own the "connective tissue"—the architecture, real-time monitoring, and mission-critical reporting.

Обязанности

System Integration & Modification: Run and maintain our real-time systems. Develop custom plugins, API integrations, and logic modifications to align the system with our growing business needs.
Infrastructure & Orchestration: Manage and scale our trading environment using Kubernetes (K8s). Ensure low-latency performance and high availability across all environments.
Observability & Reliability: Design and implement a robust monitoring stack (e.g., SigNoz, Prometheus, Grafana, ELK) to track system health, reporting latency, and execution quality in real-time.
Reporting Architecture: Build a dedicated reporting system from the ground up.
Deployment Automation: Own the CI/CD pipelines to ensure that modifications to the trading stack are deployed safely and frequently without downtime.

Требования

Backend Expertise: 3+ years of experience in a high-performance language (Go, Java, C++, Python, or Rust).
Cloud Native: Strong proficiency in Kubernetes and containerization. You should understand how to architect for "zero-downtime" deployments.
Observability: Hands-on experience with monitoring and tracing tools (e.g., Prometheus, OpenTelemetry, SigNoz, or Honeycomb).
Data Engineering: Experience building reporting or ETL pipelines using SQL/NoSQL databases (PostgreSQL, ClickHouse, or InfluxDB preferred for time-series data).
Systems Management: Comfortable reading documentation and working within the constraints/APIs of a 3rd party system.