Python / Data Engineer – Quantitative Investment Support

Singapore (Singapore)

Job Title: Python / Data Engineer – Quantitative Investment Support

Location: Onsite – Singapore

Employment Type: Contract

Experience Required: 4–8 years

About the Role

We are looking for a highly skilled Python/Data Engineer with strong experience in data manipulation, statistics, and machine learning models to support our Quantitative Investment team. The ideal candidate should be comfortable working with dataframes, DuckDB (or similar technologies), and have a good foundation in quantitative analysis. This role involves building, maintaining, and optimizing data pipelines and providing analytical support for investment research.

Key Responsibilities

• Build, optimize, and maintain data pipelines and workflows for quantitative investment strategies.
• Perform data manipulation, cleaning, and transformation using Python (Pandas, Polars, DuckDB).
• Work closely with quantitative researchers and investment professionals to provide high-quality datasets and analytical tools.
• Support the development and validation of statistical and ML models for investment decision-making.
• Ensure data quality, accuracy, and reliability across multiple data sources.
• Collaborate with stakeholders to translate investment requirements into technical solutions.
• Automate data extraction, feature engineering, and reporting tasks to improve research efficiency.
• Contribute to performance monitoring and backtesting frameworks for quantitative models.

Required Skills & Qualifications

• Bachelor’s/Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or related field.

• 4–8 years of hands-on experience in Python programming with strong knowledge of dataframes (Pandas/Polars) and DuckDB/SQL.

• Strong understanding of statistics, probability, and ML models (regression, classification, time series).

• Experience working with financial datasets and supporting quantitative research is preferred.

• Familiarity with data visualization libraries (Matplotlib, Seaborn, Plotly).

• Good understanding of data structures, algorithms, and performance optimization.

• Strong problem-solving and analytical skills.

• Exposure to quantitative finance, portfolio optimization, or risk modeling.

• Experience with cloud platforms (AWS/Azure/GCP) for data engineering.

• Familiarity with big data technologies (Spark, Dask, PyArrow).

• Knowledge of backtesting frameworks and financial modeling tools.

• Experience in DevOps/CI-CD for data workflows.

• Ability to work in a fast-paced, research-driven environment.

• Strong communication skills to collaborate with quants, data scientists, and portfolio managers.

• Detail-oriented with a focus on data quality and reproducibility.

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