Data Engineer

Key Responsibilities

• Build, deploy, and maintain NLP pipelines to extract investment‑relevant events and signals from large volumes of unstructured text (including news, regulatory filings, and research reports).
• Design and implement scalable data models and analytical frameworks to support both quantitative and qualitative investment research.
• Leverage AWS services for storage, processing, orchestration, and operationalization of data workflows.
• Partner with investment analysts and data teams to define event taxonomies and ensure extracted outputs align with research and business requirements.
• Perform exploratory data analysis and feature engineering to improve downstream predictive models and alpha‑generation efforts.
• Uphold strong standards for code quality, reproducibility, documentation, and data integrity throughout the development lifecycle.
• Communicate findings, methodologies, and recommendations effectively to both technical and non‑technical stakeholders.

Required Skills & Experience

• 4+ years of hands‑on experience in data science.
• Proven expertise in extracting structured events from unstructured text using NLP techniques.
• Strong modelling capabilities across supervised and unsupervised learning methods.
• Advanced proficiency in Python, with solid SQL skills for data manipulation, extraction, and pipeline integration.
• Practical experience working with AWS or other major cloud platforms in data engineering or advanced analytics environments.
• Clear communication skills with the ability to translate technical work into actionable insights for cross‑functional teams.
• Demonstrated track record of working with large datasets and deploying data products in scalable production environments.