Site Reliability Engineer - Data at Qube Research & Technologies

📍 Job Overview

Job Title: Site Reliability Engineer - Data
Company: Qube Research & Technologies
Location: Hong Kong
Job Type: On-site
Category: DevOps Engineer
Date Posted: 2025-06-26
Experience Level: Entry-level to Mid-level (0-2 years)

🚀 Role Summary

Key Responsibilities: Enhance real-time data processing infrastructure performance, reliability, and scalability.
Key Skills: Proficiency in server-side programming languages, infrastructure automation, monitoring tools, distributed systems, and cloud platforms.
📝 Enhancement Note: This role focuses on improving data processing infrastructure, requiring strong technical skills and a collaborative mindset.

💻 Primary Responsibilities

Collaborate with Developers and Data Users: Enhance user experience with a focus on performance, availability, and usability.
Design and Build Automation Systems: Reduce manual operations, improve deployment processes, and enforce best practices across the stack.
Contribute to Benchmarking Frameworks: Evaluate and ensure the efficiency of distributed systems and data pipelines under load.
Develop and Maintain Monitoring Tools: Tailor real-time monitoring, observability, and alerting tools to internal users and data services needs.
Participate in Incident Analysis and Resolution: Promote a culture of blameless post-mortems and continuous improvement.

🎓 Skills & Qualifications

Education: Degree in Computer Science or Engineering field.

Experience: 0-2 years of relevant experience in infrastructure, data processing, or a related field.

Required Skills:

Proficiency in at least one server-side programming language (C++, Rust, Java, Python, etc.)
Strong interest in infrastructure automation and CI/CD systems
Proficiency with monitoring stacks (Prometheus, Grafana, ELK, OpenTelemetry) and building custom dashboards/alerts
Comfortable with Linux-based systems, networking, containerization, and Kubernetes
Solid grasp of distributed systems, service-level objectives (SLOs), and performance tuning under real-time constraints
Familiarity with cloud platforms and hybrid deployments
Strong problem-solving skills, autonomy, and ability to thrive in a fast-paced, global environment

Preferred Skills:

Experience with Rust
Familiarity with data processing pipelines and big data technologies

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

Demonstrate proficiency in server-side programming languages with relevant projects.
Showcase automation scripts, CI/CD pipelines, and monitoring tools implementation.
Highlight real-time data processing, distributed systems, and performance tuning projects.

Technical Documentation:

Document code quality, commenting, and documentation standards.
Explain version control, deployment processes, and server configuration.
Describe testing methodologies, performance metrics, and optimization techniques.

💵 Compensation & Benefits

Salary Range: HKD 450,000 - HKD 600,000 per annum (Based on experience and market standards for entry-level to mid-level DevOps roles in Hong Kong)

Benefits:

Competitive salary and bonus structure
Comprehensive health insurance and wellness programs
Retirement savings plan with company matching
Generous time off and flexible working arrangements
Opportunities for professional development and growth

Working Hours: Full-time (40 hours/week) with flexible working hours and remote work options available.

🎯 Team & Company Context

🏢 Company Culture

Industry: Global quantitative and systematic investment management.

Company Size: Medium to large (100-500 employees)

Founded: 2008 (17 years ago)

Team Structure:

Data team with a focus on real-time data processing and infrastructure.
Collaborative, cross-functional teams working on data, research, technology, and trading.
Global teams working across different time zones and locations.

Development Methodology:

Agile/Scrum methodologies with sprint planning for data projects.
Code review, testing, and quality assurance practices.
Deployment strategies, CI/CD pipelines, and server management.

Company Website: Qube Research & Technologies

📈 Career & Growth Analysis

Web Technology Career Level: Entry-level to Mid-level DevOps Engineer focusing on real-time data processing infrastructure.

Reporting Structure: Reports directly to the Head of Data Engineering or a similar role, working closely with data users, developers, and other DevOps team members.

Technical Impact: Directly impacts the performance, reliability, and scalability of real-time data processing infrastructure, enabling better data-driven decision-making and investment strategies.

Growth Opportunities:

Develop expertise in Rust and other relevant programming languages.
Gain experience in data processing pipelines, big data technologies, and machine learning infrastructure.
Progress to senior or leadership roles within the DevOps or data engineering teams.

🌐 Work Environment

Office Type: Modern, collaborative office spaces designed to facilitate teamwork and innovation.

Office Location(s): Hong Kong, with global offices in major financial hubs.

Workspace Context:

Access to multiple monitors, testing devices, and development tools.
Collaborative workspace with opportunities for cross-functional interaction and knowledge sharing.
Flexible work arrangements, including remote work options.

Work Schedule: Full-time (40 hours/week) with flexible working hours and remote work options available.

📄 Application & Technical Interview Process

Interview Process:

Technical Phone/Video Screen: Assessment of programming language proficiency, problem-solving skills, and understanding of infrastructure automation.
On-site Technical Deep Dive: In-depth discussion of real-time data processing, distributed systems, and performance tuning. Hands-on coding or system design exercises may be included.
Behavioral and Cultural Fit Interview: Assessment of communication skills, teamwork, and cultural fit within the organization.
Final Decision and Offer: Based on overall performance and fit within the team.

Portfolio Review Tips:

Highlight relevant projects demonstrating proficiency in server-side programming languages, automation, and monitoring tools.
Showcase real-time data processing, distributed systems, and performance tuning projects.
Prepare for questions about system design, architecture, and trade-offs.

Technical Challenge Preparation:

Brush up on programming language concepts, data structures, and algorithms.
Practice system design exercises focusing on real-time data processing, distributed systems, and performance tuning.
Prepare for behavioral questions related to problem-solving, collaboration, and adaptability.

ATS Keywords: (Organized by category)

Programming Languages: C++, Rust, Java, Python
Infrastructure Automation: Ansible, Terraform, Puppet, Chef
Monitoring Tools: Prometheus, Grafana, ELK, OpenTelemetry
Distributed Systems: Kubernetes, Docker, Apache Kafka, Apache Cassandra
Cloud Platforms: AWS, GCP, Azure
Soft Skills: Problem-solving, Collaboration, Communication, Adaptability
Industry Terms: Site Reliability Engineering, DevOps, Data Processing, Real-time Systems

🛠 Technology Stack & Web Infrastructure

Programming Languages:

C++, Rust, Java, Python

Infrastructure Automation Tools:

Ansible, Terraform, Puppet, Chef

Monitoring Tools:

Prometheus, Grafana, ELK, OpenTelemetry

Distributed Systems:

Kubernetes, Docker, Apache Kafka, Apache Cassandra

Cloud Platforms:

AWS, GCP, Azure

📝 Enhancement Note: The technology stack may evolve based on the organization's needs and industry trends. Stay updated with the latest tools and technologies relevant to real-time data processing and infrastructure.

👥 Team Culture & Values

Web Development Values:

User Focus: Prioritize user experience and usability in all data processing and infrastructure decisions.
Performance Optimization: Continuously improve the performance, reliability, and scalability of real-time data processing infrastructure.
Collaboration: Work closely with data users, developers, and other DevOps team members to ensure efficient and effective data processing.
Continuous Learning: Stay updated with the latest tools, technologies, and best practices in real-time data processing and infrastructure.

Collaboration Style:

Cross-functional Integration: Collaborate with data users, developers, and other DevOps team members to ensure efficient and effective data processing.
Code Review Culture: Encourage peer review and knowledge sharing to improve code quality and maintain best practices.
Knowledge Sharing: Foster a culture of continuous learning and mentoring to help team members grow and develop their skills.

⚡ Challenges & Growth Opportunities

Technical Challenges:

Real-time Data Processing: Design and implement efficient, real-time data processing pipelines to support the organization's investment strategies.
Distributed Systems: Scale and optimize distributed systems to handle increasing data volumes and processing demands.
Performance Tuning: Continuously monitor and optimize real-time data processing infrastructure for improved performance and reliability.
Emerging Technologies: Stay updated with the latest tools, technologies, and best practices in real-time data processing and infrastructure.

Learning & Development Opportunities:

Technical Skill Development: Gain expertise in Rust and other relevant programming languages, data processing pipelines, and big data technologies.
Conferences and Certifications: Attend industry conferences, obtain relevant certifications, and engage with professional communities to stay updated with the latest trends and best practices.
Mentorship and Leadership: Seek mentorship opportunities to develop leadership skills and prepare for senior or management roles within the organization.

💡 Interview Preparation

Technical Questions:

Programming Language Proficiency: Prepare for questions assessing your understanding of C++, Rust, Java, or Python, focusing on data structures, algorithms, and problem-solving.
System Design: Brush up on system design principles, patterns, and trade-offs, focusing on real-time data processing, distributed systems, and performance tuning.
Problem-solving: Practice solving complex problems related to real-time data processing, distributed systems, and performance tuning.

Company & Culture Questions:

Company-specific Challenges: Research the organization's data processing challenges and prepare for questions about how you would address them.
Collaboration and Communication: Prepare for questions assessing your ability to work effectively with data users, developers, and other DevOps team members.
Adaptability: Demonstrate your ability to thrive in a fast-paced, global environment and adapt to changing priorities and requirements.

Portfolio Presentation Strategy:

Project Walkthrough: Prepare a structured walkthrough of your relevant projects, highlighting your role, the technologies used, and the outcomes achieved.
Code Explanation: Be ready to explain your code, design decisions, and trade-offs, demonstrating your technical depth and problem-solving skills.
User Impact: Highlight the user impact of your projects, demonstrating your understanding of user experience and usability in data processing and infrastructure.

📌 Application Steps

To apply for this Site Reliability Engineer - Data position:

Submit your application through the Qube Research & Technologies careers page.
Customize your resume and portfolio to highlight relevant skills, projects, and achievements in server-side programming, infrastructure automation, and real-time data processing.
Prepare for technical interviews by brushing up on programming language concepts, system design, and problem-solving techniques.
Research the organization's data processing challenges and company culture to demonstrate your understanding and fit within the team.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.

Site Reliability Engineer - Data