Data developer

We are an early-stage quantitative trading team with core members coming from top-tier hedge funds, both domestically and internationally. Distinct from traditional quantitative approaches, we fully embrace an AI‑first research paradigm, applying deep learning and large‑scale compute to accelerate factor discovery and model prediction.

We believe that competition at the highest level of quantitative investing is fundamentally a competition in research efficiency and model sophistication. A data system with high accuracy and low latency not only determines the quality and productivity of research, but also defines the long‑term ceiling a quantitative firm can reach.

We are currently at a critical stage of system construction and are seeking developers with strong engineering foundations to jointly build industry‑leading AI‑driven quantitative research infrastructure.

Responsibilities

1. Data Architecture Design and Implementation
   Design and build the overall architecture of the quantitative data platform, developing a high‑performance storage and computation system covering market data, financial data, and alternative data, based on OLAP architectures such as ClickHouse.

2. Complex Data Processing Logic Development
   Design and implement high‑performance data aggregation, filtering, and transformation logic. Develop efficient computational operators tailored to quantitative research needs, supporting large‑scale data preprocessing and feature extraction.

3. Multi‑Source Data Pipeline Construction
   Build high‑throughput ETL pipelines for full‑market A‑share high‑frequency data (Tick / Level‑2), financial statements, and large‑scale text data (e.g. news, sentiment, research reports), addressing performance bottlenecks in cleaning and standardizing massive heterogeneous datasets.

4. Fully Automated Data Monitoring System
   Develop an end‑to‑end automated monitoring and alerting framework, with a particular focus on common quantitative research challenges such as point‑in‑time correctness and data alignment, enabling real‑time detection, diagnosis, and closed‑loop handling of data anomalies.

5. Cross‑Project Engineering Support
   As a core member of a startup‑stage team, flexibly support other critical engineering initiatives as needed, such as compute cluster scheduling optimization and research toolchain development, jointly driving rapid evolution of the overall technology stack.

Requirements

1. Core Computer Science Fundamentals

• Solid foundations in algorithms and data structures, with strong understanding of computer architecture, memory management, and I/O performance optimization.

2. Programming and Databases

• Proficient in Python, with the ability to write high‑performance Pandas / Polars / NumPy code, and a strong understanding of their underlying execution mechanisms and memory layout.
• Highly proficient in SQL, with hands‑on production experience optimizing ClickHouse or other distributed columnar databases.
• Familiar with Linux environments, and capable of maintaining high engineering standards, including Git, code review, and CI/CD.

3. Engineering Mindset

• Attention to detail: Extremely high standards for data accuracy, with the ability to systematically identify and resolve data issues in edge cases.
• Continuous learning ability: Strong curiosity about new technologies and tools in quantitative trading and AI, with high self‑motivation and the ability to rapidly learn and apply cutting‑edge techniques to solve complex real‑world problems.