Data Science Research Assistant / Data Lake Engineer

The position supports the development of a research data lake for empirical work with large-scale financial, textual, licensed, and partly confidential datasets. The objective is to build a robust, well-documented, and reproducible data infrastructure that allows researchers to ingest, store, process, document, and analyze data efficiently and securely. Research and infrastructure tasks will include data engineering, coding, documentation, and coordination with researchers and IT/platform providers. Core tasks include, among others: Design and implementation of the research data lake, Data ingestion and integration, Automation of research pipelines, Data governance, confidentiality, and access management, Research support. The position is particularly suitable for a candidate who wants to combine data science, data engineering, and applied academic research. The role offers the opportunity to build a research infrastructure from the ground up and to gain experience with large-scale, real-world research data.

The University of St. Gallen is a leading business university with over 10,000 students and 3,700 employees. The Swiss Institute for International Economics and Applied Economic Research, or SIAW-HSG for short, has around 40 employees and is one of the university's 36 institutes. Our focus is on foreign trade, macroeconomics, taxation and social systems, public economics, environmental economics, financial economics and insurance. We are responsible for research, teaching and services in our fields and train young talent for research and the interface between science and practice. The chair for International Economics at the SIAW has expertise insurance, banking, and systemic risk, with an emphasis on connecting academic insights and regulatory practice.

• Master's degree in data science, computer science, statistics, econometrics, information systems, or a closely related field

• Strong interest in research data infrastructure, data engineering, automation of empirical research pipelines, and reproducible science

• Excellent programming skills, preferably in Python and SQL; experience with R, Stata, or Matlab is an asset

• Experience with data engineering tools and workflows, such as APIs, ETL/ELT pipelines, Git, Docker, workflow automation, metadata documentation, or cloud-based research environments

• Familiarity with structured and unstructured data, including financial datasets, text data, and large-scale file systems

• Strong understanding of data governance, access control, documentation, and reproducibility

• Willingness to work carefully with licensed and confidential research data

• High motivation and ability to work independently as well as in close collaboration with researchers and IT/data infrastructure providers

• Prior experience with cloud-based data science platforms is an advantage