Data Science Research Assistant / Data Lake Engineer (m/f/d)

Job description

The University of St. Gallen is a top business university in Switzerland. It's a great opportunity to thrive in an innovative academic environment. Tasks Support developing a research data lake for large datasets. Automate workflows for data ingestion and integration efficiently. Assist researchers with data analysis and documentation tasks. Skills Master's in data science, computer science, or related field required. Strong programming skills in Python and SQL essential. Experience with data engineering tools and workflows preferred. 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. Terms of Employment Start date: 1 August 2026 or by agreement Fixed-term for 6 months, with the possibility of extension for another fixed-term period of 6 months depending on project needs and performance Compensation: Competitive salary in line with Swiss university standards Work environment: The position is embedded in an academic research environment and involves close collaboration with faculty, PhD students, research assistants, and IT/data infrastructure partners Application Requirements To start the application process, please submit: A motivation letter explaining your interest in the position and relevant experience with data science, data engineering, or research infrastructure, with concrete examples of the projects you worked on and your roles in those projects A CV Academic transcripts, especially from the Master's degree A sample of technical work, such as a GitHub repository, coding project, data pipeline, thesis, seminar paper, or other relevant work sample Names of up to two academic or professional referees Applications will be reviewed on a rolling basis until the position is filled. Your tasks Responsibilities and Project 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 Support the design of a scalable data architecture for approximately 5 TB of research data Structure data into raw, cleaned, and analysis-ready layers Develop clear naming conventions, folder structures, access rules, and documentation standards Ensure that the data lake supports long-term retention of raw and processed data Data ingestion and integration Build automated workflows to import data from external providers, databases, APIs, file deliveries, and researcher-maintained sources Integrate financial datasets, textual datasets, and other licensed research data into a consistent infrastructure Implement validation checks, logging, error handling, and version control for data updates Document data provenance, licenses, update frequencies, and usage restrictions Automation of research pipelines Develop reproducible pipelines for cleaning, transforming, and preparing datasets for empirical research Create reusable scripts and templates for recurring data tasks Support researchers in converting manual data work into automated and documented workflows Contribute to reproducible research practices through Git-based code management and clear pipeline documentation Data governance, confidentiality, and access management Help implement procedures for handling licensed and confidential datasets Support role-based access concepts, documentation of data permissions, and compliance with provider agreements Prepare data inventories and metadata files to make datasets findable and usable by the research team Coordinate with internal IT or external platform providers where needed Research support Assist researchers with data preparation, quality checks, exploratory analysis, and technical troubleshooting Provide documentation and short internal guides so that the infrastructure can be maintained beyond the initial project phase Contribute to other data-intensive research projects at the Chair or Institute where appropriate 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. Your profile 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 "A place where knowledge is created" - As one of Europe's leading universities of economics and business administration, the University of St.Gallen (HSG), Switzerland, is committed to the education of over 10'000 students. The HSG is one of the largest employers in the region and provides an attractive and innovative environment for more than 3'500 researchers, educators and professional staff.