Quantitative Data Engineer
Dublin City Centre
About the Company
Our client is a global financial services and technology leader that leverages cutting‑edge, innovative platforms to solve highly complex challenges. Their in‑house software runs on the latest technologies and is designed to address diverse problems across the business. Currently, they are in the early phases of a transformative initiative aimed at redefining the industry, and they seek top‑tier talent to help achieve this ambitious goal.
About the Position
This is a full‑time, permanent role based in Dublin City Centre, requiring five days a week on site.
The Quantitative Engineering and Data (QED) group in Dublin is a newly established team dedicated to building tools, processes, and datasets that fuel data‑driven revenue generation. The QED Dublin team plays a pivotal role in accelerating innovation across the company. In this position, you will collaborate with Quantitative Research, Trading, and Engineering teams in Dublin and globally to analyse complex datasets, create high‑quality data resources, and support research and trading activities. As your expertise grows, you will gain a deep understanding of the quantitative trading business and use your data capabilities to make meaningful contributions to the firm.
Key Responsibilities
Support research and trading operations by applying data science, analytics, and engineering know‑how.
Source, clean, and manage datasets while designing and maintaining robust data pipelines.
Partner with researchers throughout the development cycle, taking on tasks that match your strengths to enhance productivity.
Improve the research process by automating workflows and introducing new tools or methodologies.
Develop a comprehensive understanding of the company's systems and core business functions.
Experience and Requirements
Degree in a STEM discipline, with evidence of independent work via professional experience, MSc, or PhD.
3+ years of professional Python experience, with strong skills in NumPy, Pandas, and other scientific libraries.
Hands‑on experience working in a Linux environment.
Genuine enthusiasm for working alongside researchers to enhance and speed up their projects.
Proven ability to work independently, remain self‑motivated, and spot opportunities for process improvements.
Excellent communication and interpersonal skills to collaborate effectively in a quantitative setting.
Strong attention to detail and adaptability to shifting priorities.
Interest in quantitative trading and financial markets.