Data Engineer

Responsibilities

• Data Architecture Design & Implementation
  Design and build the core quantitative data platform, covering market data, financial data, and alternative data, with a focus on high-performance storage and computation (e.g. OLAP architectures such as ClickHouse).

• High-Performance Data Processing Logic
  Design and implement efficient data aggregation, filtering, and data-flow logic. Develop optimized computation operators to support large-scale data preprocessing and feature extraction for quantitative research.

• Multi-Source Data Pipelines (ETL)
  Build high-throughput, concurrent data pipelines to process:

  • Full A-share market high-frequency data (Tick / Level-2),
  • Financial statements,
  • Large-scale text data (news, research reports, sentiment, etc.).
    Address performance bottlenecks in data cleaning, normalization, and standardization for massive heterogeneous datasets.

• Automated Data Monitoring & Quality Control
  Build an end-to-end automated monitoring and alerting system, with a strong focus on common quant data challenges such as point-in-time correctness and data alignment, enabling real-time anomaly detection, diagnosis, and closed-loop resolution.

• Cross-Project Engineering Support
  As a core member of a lean startup team, flexibly support other key engineering initiatives as the business evolves, such as compute cluster scheduling optimization or research tooling development, and help drive rapid iteration of the overall tech stack.

Requirements

• Strong Computer Science Fundamentals
  Solid foundation in algorithms and data structures, with a good understanding of computer architecture, memory management, and I/O performance tuning.

• Programming & Databases

  • Proficient in Python, capable of writing high-performance code using Pandas / Polars / NumPy, with a solid understanding of their internal execution and memory models.
  • Advanced SQL skills, with hands-on experience tuning ClickHouse or other distributed columnar databases in production environments.
  • Strong Linux background with good engineering practices (Git, code review, CI/CD).

• Engineering Mindset

  • Detail-oriented: Extremely high standards for data accuracy and consistency; able to systematically identify and resolve edge-case data issues.
  • Fast learner & self-driven: Strong curiosity about quantitative trading and AI technologies, with the ability to quickly master and apply new tools to solve complex real-world problems.

Nice to Have

• Experience processing high-frequency market data, especially A-share Level-2 tick and order book data, and common reconstruction scenarios.
• Deep experience in database performance tuning, including storage engine configuration, complex query optimization, and distributed cluster scaling.
• Engineering automation experience, such as CI/CD pipeline setup, containerization, and infrastructure automation to improve development and delivery efficiency.