Company Description DRFZ Berlin is a research-focused organization dedicated to advancing knowledge and innovation in the biomedical and health sciences. Located in Berlin, DRFZ collaborates with national and international partners to drive data-informed decision-making and support cutting-edge scientific projects. Our Bioinformatics and High-Performance Computing platform maintains a modern research infrastructure, including digital systems and decentralized data platforms that enable large-scale, secure data analysis. Team members work in an interdisciplinary environment that values collaboration and cordiality, scientific rigor, international-friendly and inclusive environment, and continuous learning.
Role Description The Staff Bioinformatician / Data Engineer for this CP4 BioBlock Consortium project is a full-time (100%), on-site position based in DRFZ Berlin (with co-supervisors at the TU Berlin's Bioprocess Engineering department and KIWI Biolabs). This role offers an initial 2-year contract on the TVöD E13 salary scale, with the possibility of extension or transitioning into a PhD track if desired.
Unlike a purely academic research role, this position is heavily focused on IT engineering, software development, and data infrastructure. The primary responsibilities involve expanding a graph database to integrate fresh clinical patient data for upcoming publications and industry collaborations. Additionally, you will design and build a simulation GUI to help wet-lab staff scientists model their datasets (such as RNA-seq, proteomics, and flow cytometry) to optimize sequencing resources and minimize waste. The role also requires establishing robust data governance protocols and implementing blockchain integration strategies to ensure secure, FAIR-compliant medical data exchange across the consortium.
Note: Given the highly interdisciplinary nature of this role, we do not expect any single candidate to be an expert in every area listed. We evaluate applications holistically based on your unique blend of skills. If you possess a strong technical foundation in several of these areas and have a demonstrated readiness to learn the rest, we highly encourage you to apply.
Qualifications
Cultural and Interpersonal Skills (non-negotiable):
- A strong growth mindset with high receptivity to constructive feedback and a proactive, open-minded approach to continuous learning and skill refinement. In simple terms: attitude is more important than technical skill, since one limits the other, and technical skill learning we will provide.
- Exceptional collaborative skills and a demonstrated commitment to respectful, inclusive teamwork. You must be able to thrive in and contribute positively to a diverse, international, and female-led working environment.
Formal certifications and / or skills:
- Master’s degree in Computer Science, Data Engineering, Bioinformatics, Information Systems, or a related technical field.
- Strong skills in database architecture and data modeling, with specific hands-on experience using graph databases (Neo4j). (Ideal, but not required. If you learn fast, you are welcome to be trained here).
- Proven proficiency in GUI development and building accessible, user-friendly dashboards or tools for non-technical users. (Ideal, but not required).
- Knowledge of at least one programming language commonly used in data engineering and application development (e.g., Python, Java, JavaScript, or Dart). Preferrably Python.
- Familiarity with or strong interest in blockchain technologies, smart contracts, and decentralized data networks. (Emphasis in strong interest, since for many applicants might be a new topic, which is OK if they are willing to learn).
- Understanding of the concepts of data governance, FAIR principles, data security.
- A hands-on, problem-solving mindset geared toward infrastructure and tool-building.
- Ability to collaborate effectively with an interdisciplinary team of wet-lab scientists, bioinformaticians, IT specialists, engineers, postdoctoral researchers, and industry partners.
- Previous experience in a healthcare, bioinformatics, or highly regulated scientific environment is a strong advantage, but not required.