Python / Data Engineer – Quantitative Investment Support

Python / Data Engineer – Quantitative Investment Support

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 skilledPython/Data Engineerwith strong experience indata manipulation, statistics, and machine learning modelsto support ourQuantitative Investment team. The ideal candidate should be comfortable working withdataframes, 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 maintaindata pipelines and workflowsfor quantitative investment strategies.
• Performdata manipulation, cleaning, and transformationusing Python (Pandas, Polars, DuckDB).
• Work closely withquantitative researchers and investment professionalsto provide high-quality datasets and analytical tools.
• Support the development and validation ofstatistical and ML modelsfor investment decision-making.
• Ensuredata quality, accuracy, and reliabilityacross multiple data sources.
• Collaborate with stakeholders totranslate investment requirements into technical solutions.
• Automate data extraction, feature engineering, and reporting tasks to improve research efficiency.
• Contribute toperformance monitoring and backtesting frameworksfor quantitative models.

Required Skills & Qualifications

• Bachelor’s/Master’s degree inComputer Science, Data Science, Statistics, Mathematics, or related field.
• 4–8 years of hands‑on experience inPython programmingwith strong knowledge ofdataframes (Pandas/Polars)andDuckDB/SQL.
• Strong understanding ofstatistics, probability, and ML models(regression, classification, time series).
• Experience working withfinancial datasetsand supporting quantitative research is preferred.
• Familiarity withdata visualization libraries(Matplotlib, Seaborn, Plotly).
• Good understanding ofdata structures, algorithms, and performance optimization.
• Strong problem‑solving and analytical skills.

• Exposure toquantitative finance, portfolio optimization, or risk modeling.
• Experience withcloud platforms (AWS/Azure/GCP)for data engineering.
• Familiarity withbig data technologies(Spark, Dask, PyArrow).
• Knowledge ofbacktesting frameworks and financial modeling tools.
• Experience inDevOps/CI-CD for data workflows.

• Ability to work in afast‑paced, research‑driven environment.
• Strong communication skills to collaborate withquants, data scientists, and portfolio managers.
• Detail‑oriented with a focus ondata quality and reproducibility.