Senior Full-Stack Engineer, Research Analytics
Own a whole product: take our research-analytics product for universities to production and grow it from there.
Saarbrücken, hybrid · Full-time · Start: January 2027
The role
You own our research-analytics product for universities and research institutes, end to end. It turns open scholarly data into an always-current, AI-built view of an institution’s research. You take the new version to production and grow it with the institutions that use it.
Why this role
- It is your product: architecture, data, interface and production are all yours.
- Real users early: what you build goes straight to the institutions that use it.
- Rich open data: publications and researcher profiles from sources such as OpenAlex, Crossref, PubMed and ORCID, enriched and scored with LLMs.
What you will do in your first year
- Get the new version through its acceptance tests on real institutional data, with strict isolation between institutions.
- Own the data pipelines and the reports they feed: ingestion, transformation, enrichment and AI-based scoring of research outputs. In the first months a working student runs ingestion and onboarding for new institutions under your lead.
- Onboard institutions and support them as the product grows.
- Build the customer portal and the internal operations console on the product’s design system.
- Take the product to production in the cloud, with backups, monitoring and a clean security baseline.
- The stack
- Python, FastAPI and Temporal; MongoDB and MinIO; React with TypeScript, Tailwind, Radix, Storybook and ECharts; Docker and Kubernetes; and whichever AI models are best for each task.
What you bring
- Several years designing, owning and running full-stack products in production. Show us one.
- Python backends and React with TypeScript frontends, both at a professional level.
- Multi-tenant data design, and the discipline it takes.
- Data pipelines and production use of LLM APIs: ingestion from public APIs, structured outputs, cost control.
- Ownership: you have run a system end to end, deployments and incidents included.
- You turn a vague goal into a precise work order an AI coding agent can build from, and you can show us a time you caught an agent’s code or tests being wrong.
Nice to have
Temporal; MongoDB; scholarly data sources (OpenAlex, Crossref, ORCID, PubMed); GDPR-compliant use of LLMs; design systems.
Apply.
The full advert, the terms and the application form are on Indeed. If you would rather write to us directly, send your CV and one link that shows your work — code, a paper, or a project you built.