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. 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.
Tasks
- Design and implement 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 naming conventions, folder structures, access rules, and documentation standards.
- Ensure long-term retention of raw and processed data.
- 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.
- 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.
- 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.
- 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.
Requirements
- 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.
Benefits
- Inspirational work environment.
- Executive education network.
- Family & career support, including part-time positions, 16 weeks of maternity leave, subsidised crèche, and childcare during vacations.
- Equal opportunities for all employees and students.
- Fit and healthy: diverse sports activities, campaign weeks for corporate health management, fruit in the workplace, and discounted meals in the canteen.
- Modern IT infrastructure and nationwide access to scientific information via HSGswisscovery.
- Support for internal and external executive education opportunities, including seminars, e-learning, coaching, and collegial advice.
- Flexible work time models with a variable number of hours per week.
- Culture of cross-functional collaboration and space for forging valuable relationships, including regular exchanges and internal events.
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Über uns
Das Team
The position is embedded in an academic research environment and involves close collaboration with faculty, PhD students, research assistants, and IT/data infrastructure partners. The chair for International Economics at the SIAW has expertise in insurance, banking, and systemic risk, with an emphasis on connecting academic insights and regulatory practice.