We are looking for a meticulous and driven Data Engineer to serve as the backbone of our Quant Fund. You will be responsible for the data of our trading strategies. You will build, scale, and maintain high-frequency and alternative data pipelines that power our U.S. Equities strategies. The ideal candidate treats data architecture as a craft, ensuring every tick, trade, and corporate action is captured with 100% fidelity.
Key Responsibilities
- Pipeline Architecture: Design and deploy robust ETL/ELT pipelines to ingest large-scale market data (OHLCV, Level 2 Quotes, Trades) and non-market data (News, Social Sentiment, Fundamentals).
- Data Integrity \& Validation: Implement rigorous automated data quality checks to detect anomalies, look-ahead bias, and survival bias in financial time-series.
- Storage Management: Optimize data storage using ClickHouse for high-speed analytical queries, PostgreSQL for relational metadata, and Parquet/S3 for scalable data lakes.
- API Integration: Build and maintain connectors for financial data providers (e.g., Bloomberg, Refinitiv, Polygon, Alpaca) and alternative data vendors.
- Collaboration: Work closely with Quant Researchers to ensure datasets are "research-ready" and optimized for backtesting and live trading execution.
- Monitoring: Develop real-time monitoring and alerting systems to ensure pipeline uptime and data freshness.
Technical Requirements
- Languages: Expert-level Python (Pandas, Polars, NumPy) and advanced SQL.
- Environment: Strong proficiency in Linux/Unix environments and shell scripting.
- Databases: Experience with columnar databases (ClickHouse preferred) and traditional RDBMS (PostgreSQL).
- Data Engineering: Familiarity with modern data stack tools (Airflow, Dagster, or Prefect) and cloud storage (AWS S3).
- Financial Domain: Solid understanding of U.S. Equity market mechanics, including corporate actions, splits/dividends adjustments, and T+1/T+0 settlement logic.
Preferred Qualifications
- Experience working in a Hedge Fund, Prop Trading Firm, or Fintech environment.
- Knowledge of low-latency data formats (HDF5, Zarr) or messaging queues (Kafka/RabbitMQ).
- Familiarity with containerization (Docker/Kubernetes).
- A passion for financial markets and quantitative finance.
Job Type: Contract
Contract length: 12 months
Pay: Up to RM15,000.00 per month
Benefits:
- Health insurance
- Work from home
Work Location: Hybrid remote in Kuala Lumpur
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