Lead Data Engineer - Global Trading Firm - Hong Kong
Summary
Lead a team building and operating data pipelines and platforms for a global trading firm, ensuring reliable ingestion, processing, and delivery of market and trade data to analytics and risk systems.
Our client, a global trading firm, is actively seeking a Data Engineering Lead to lead the design, build, and operation of data platforms and pipelines that support their trading business. This role focuses on reliable data ingestion, processing, quality, and delivery to analytics and trading/risk use cases. You will lead a small data team and partner with stakeholders across Trading, Quant/Research, Risk, and Technology to drive engineering best practices around scalability, performance, and operational excellence.
The role:
- Lead the end-to-end development of data pipelines (batch and/or streaming) for market, reference, and trade-related datasets.
- Design and maintain scalable data architectures (e.g., lakehouse/warehouse patterns), ensuring data is accessible, governed, and performant.
- Own data ingestion from internal/external sources (APIs, files, message buses, vendor feeds) and manage end-to-end data flow reliability.
- Implement and oversee data quality checks, reconciliation, and monitoring (e.g., completeness, accuracy, latency, schema validation).
- Build data products for downstream consumption (dashboards, research environments, reporting, risk analytics), working closely with business users.
- Ensure production readiness: operational monitoring, incident response, runbooks, SLA/SLO alignment, and robust CI/CD.
- Lead engineering practices: code reviews, standards, reusable components, documentation, and mentoring.
- Collaborate with stakeholders to translate business requirements into technical roadmaps and measurable outcomes.
What you offer:
- 8+ years of experience in data engineering / data platform engineering
- Strong hands‑on experience in Python and SQL
- Proven experience designing and operating data pipelines in a production environment
- Strong experience with modern data tooling
- Excellent understanding of data modeling and governance concepts
- Strong experience with monitoring and operational practices for data workloads
- Experience in trading environments (market data, order/trade lifecycle, P&L/risk data, reference data)
- Cloud exposure (AWS/Azure/GCP) and infrastructure-as-code (Terraform) knowledge
- Experience leading teams and/or mentoring engineers