My client is a proprietary trading firm founded by former technologists from a leading global trading firm. With over 120 employees across Asia, Australia, and the US, they trade derivatives, ETFs, crypto, and specialize in options market making. They operate a fully on-prem Linux environment with a modern tech stack. The culture is flat, collaborative, and international, with strong work-life balance and an open-door policy.
Role Overview
We are seeking an experienced Data Systems Team Lead to lead a newly established team building the firm’s next-generation data platform for both research and real-time trading. This is a greenfield build spanning orchestration, storage, data access, and governance at scale. The role blends leadership with architecture and hands-on engineering to deliver robust, maintainable, and high-performing solutions across both hardware and software. You will set the vision for a centralized data framework and drive seamless orchestration, storage, and access across a wide range of datasets.
Key Responsibilities:
- Team Leadership: Build and lead a high-performance Data Systems Team, including mentoring and career development.
- Roadmap Ownership: Define and deliver the data platform roadmap, balancing business and technical priorities.
- Platform Architecture: Design and implement a centralized data framework and supporting applications.
- Data Pipelines: Develop robust, maintainable, and high-performing data pipelines across batch and streaming.
- Performance Optimization: Drive performance optimizations across both hardware and software for quant research and regulatory data retention.
- Infrastructure Management: Manage, maintain, and expand dedicated hardware infrastructure for data systems.
- Reliability: Ensure reliability, uptime, and effective capacity planning for data workloads.
- Stakeholder Collaboration: Partner with stakeholders across research, trading, and engineering to align data workflows with business needs.
- Data Governance: Standardize ingestion, schema management, lineage, permissions, and audit processes.
- Issue Resolution: Investigate upstream data sources, identify root causes of data issues, and implement sustainable corrective actions.
Required Skills & Experience:
- Experience: 10+ years as a Data Systems Engineer delivering production data platforms, with 2+ years leading and mentoring engineering teams.
- Pipeline Engineering: Proven ability to design, scale, and optimize data pipelines and distributed systems in production.
- Data Platform Fundamentals: Deep expertise in batch and streaming processing, scalable table/metadata formats, object storage, and SQL.
- Orchestration: Hands-on experience building scalable ingestion and orchestration workflows (e.g., reliable scheduling, dependency management, backfills, and recovery).
- Architecture: Solid understanding of modern data architecture patterns and making tradeoffs across performance, reliability, and cost.
- Storage Management: Understanding of data storage and file/table lifecycle management.
- System Design: Ability to make architectural decisions across IT solutions, system design, and implementation—ensuring maintainability and extensibility.
- Operational Excellence: Demonstrated track record of improving reliability and operational excellence for data systems.
- Communication: Excellent stakeholder communication skills, with strong roadmap ownership and ability to align engineering delivery with business needs.
- Change Management: Ability to drive large-scale change while iterating safely—planning rollouts, learning from outcomes, and adjusting direction when required.
- Problem-Solving: Strong problem-solving mindset with high attention to detail, including root-cause analysis of data/system issues.
- Industry: Prior experience in quant trading or financial services is highly desirable.
- Education: University degree in Computer Science or related discipline.
- Language: Fluency in written and spoken English.
Please send your CV to Sarah Fan at [email protected], or call +852 2315 9512 for a confidential discussion.
