• U.S. equities/ other financial datasets.
• Attractive package with well career development
• Ingesting, cleaning, validating, storing, and monitoring large-scale market data

Data Engineer (Malaysia based)

We are seeking a detail-oriented and proactive Data Engineer to form the core infrastructure of our quantitative fund. In this position, you will own the end-to-end data ecosystem that fuels our trading strategies. You will architect, expand, and support high-frequency market data pipelines alongside alternative data feeds to drive our U.S. Equities trading systems. The right candidate views data engineering as an art form and is committed to capturing every tick, trade, and corporate event with complete accuracy and reliability.

Key Responsibilities

• Architect and implement scalable ETL/ELT workflows for ingesting massive volumes of market data (including OHLCV, Level 2 order book, and trade records) as well as non-market sources such as news sentiment, social media signals, and fundamental datasets.
• Enforce strict data quality frameworks with automated validation routines to identify anomalies, prevent look-ahead bias, and eliminate survival bias in time-series financial data.
• Manage and fine-tune storage solutions — leveraging ClickHouse for ultra-fast analytics, PostgreSQL for structured metadata, and Parquet files on S3 for cost-effective, large-scale data lakes.
• Develop and maintain robust API integrations with leading financial data providers (e.g., Bloomberg, Refinitiv, Polygon, Alpaca) and various alternative data vendors.
• Partner closely with quantitative researchers to deliver clean, production-grade datasets optimized for rigorous backtesting and seamless live trading deployment.
• Build comprehensive real-time monitoring dashboards and alerting mechanisms to guarantee pipeline reliability and timely data delivery.

Technical Requirements

• Expert proficiency in Python (with deep experience in Pandas, Polars, and NumPy) combined with advanced SQL skills.
• Strong command of Linux/Unix systems and shell scripting for efficient operational workflows.
• Hands-on experience with columnar databases (ClickHouse highly preferred) and traditional relational databases such as PostgreSQL.
• Practical knowledge of modern orchestration tools (Airflow, Dagster, or Prefect) and cloud object storage platforms (AWS S3).
• In-depth understanding of U.S. equity market microstructure, including corporate actions, dividend/split adjustments, and T+1 / T+0 settlement rules.

Preferred Qualifications

• Prior experience in a hedge fund, proprietary trading firm, or fintech company.
• Familiarity with high-performance data formats (HDF5, Zarr) or real-time messaging systems (Kafka, RabbitMQ).
• Working knowledge of containerization technologies (Docker and Kubernetes).
• Genuine enthusiasm for financial markets and quantitative trading strategies.