Job Responsibilities
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Data Engineering Implementation: Execute the end-to-end backfill process for high-frequency MBO (Market-By-Order) historical data, managing everything from raw PCAP packet processing to the cleaning, standardization, and final delivery of high-fidelity MBO data.
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Data Pipeline Operations & Adaptation: Actively participate in the daily operations of a high-volume data scheduling system based on Dagster; handle routine task monitoring, troubleshooting, and recovery/re-runs; and—under mentor guidance—rapidly adapt to and implement changes in exchange-level underlying protocols.
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Data Quality Assurance: Develop data quality validation rules and automation scripts; perform initial analysis and diagnosis (first-line diagnosis) for data quality anomalies detected during daily operations.
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Quantitative Research Support: Serve as a frontline point of contact to efficiently support quantitative researchers' data needs, assisting in identifying, explaining, and resolving complex issues related to market data encountered during the research and investment process.
Job Requirements
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Background: 0-3 years relevant work experience, or an outstanding recent graduate/junior engineer with exceptional technical potential.
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Core Skills: Solid coding proficiency in C++ and Python; familiarity with Linux development environments. Experience with high-frequency tick data, time-series data, or large-scale distributed data processing is a plus.
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Familiarity with Python and common data processing libraries (e.g., pandas, numpy) is preferred.
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Strong interest in financial trading systems and data systems; excellent learning agility and teamwork skills.
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Practical engineering project experience (e.g., coursework, internships, open-source projects) is preferred.
Bonus Points
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Understanding of common market data structures (e.g., order book, tick, bar).
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Experience participating in finance-related projects, competitions, or coursework.