Senior/Staff Engineer, Liquidity Platform, Private Data Engineer

Key Responsibilities

• Own and operate the trade and position platform end-to-end — roadmap, delivery, reliability, incidents, and continuous improvement.
• Build and maintain connectors to centralized exchanges, counterparties, and on-chain sources (REST/WebSocket, blockchain data), handling the real-world complexities of external integrations.
• Develop and extend the Python + SQL data model that normalizes trade and account flows across venues.
• Deliver trusted data outputs for Finance, Operations, and the trading desk — used for verification, reporting, and analytics.
• Improve reconciliation workflows to catch and resolve discrepancies between venue statements and internal records.
• Build robust data quality controls with clear lineage, reproducibility, and documentation.
• Partner with Finance and Ops to translate business rules into testable logic and onboard new venues/products efficiently.
• Contribute to the broader data platform (Spark/Iceberg/ClickHouse/ML tooling).

Qualifications

• Bachelor's degree or above in Computer Science or a related field.
• 4-7 years in software or data engineering, ideally with exposure to trading, middle-office, or post-trade systems.
• Strong production Python skills, ideally with Spark/PySpark experience, with a focus on code quality and operational reliability.
• Strong SQL skills; hands-on MySQL and PostgreSQL experience (schema design, performance tuning).
• Experience integrating with external APIs (REST/WebSocket) and navigating real-world constraints.
• Familiarity with trade lifecycle concepts — positions, fees, funding, and reconciliation.
• A controls mindset: you care about data quality, reproducibility, and documentation.
• Proven ability to own production systems end-to-end — from incident response to stakeholder communication.
• Strong communication skills; comfortable working across business and technical teams, aligning on requirements and data delivery.
• Fluency in Chinese and English.

Preferred Skills

• Experience with workflow orchestration tools (Airflow, Celery, or similar).
• Exposure to Kubernetes (k8s) for containerized deployments.
• Exposure to modern data platforms (Iceberg, ClickHouse) or ML tooling (MLflow, TensorFlow).