(Senior) Research Data Manager

Company Summary: mbiomics was founded in 2020 by an experienced founding team with the vision to deliver effective microbiome-based therapeutics that will revolutionize the treatment of many chronic diseases. We recently closed a Series A funding, which enables us to start the development of game-changing live-bacterial therapeutics (LBT). mbiomics is leveraging its tailored microbiome analysis platform to overcome challenges in LBT development – by providing precision profiling data for improved bacterial consortia selection, patient stratification, and patient monitoring for clinical trials. Position Summary: You will be responsible for establishing mbiomics’ data governance framework while also building the data infrastructure to make research and clinical data usable, compliant, and AI/ML-ready. In this role you will set standards, implement cloud-based solutions, and collaborate closely with scientists to improve the capture, organization, and accessibility of our data. As a growing startup, this position combines research and clinical data governance leadership with hands-on implementation. You will also play a key role in preparing mbiomics for the next generation of AI/ML applications by ensuring data is standardized, annotated, and interoperable, and by enabling teams to make effective use of enterprise AI tools. Responsibilities/Duties: Governance & Compliance Define and implement data governance policies (data lifecycle, data security, data integrity, metadata, standards, access, retention, lineage, alignment to infrastructure). Promote FAIR data principles across all R&D teams. Ensure compliance with data protection regulations (e.g., GDPR, EU IA act, HIPAA when applicable). Create governance documentation and ensure adoption of good practices across the company. Implementation & Infrastructure Build and maintain pipelines/workflows for ingesting, organizing, and validating data (GCP/BigQuery preferred). Automate repetitive tasks such as metadata capture, file organization, and ontology mapping. Put in place a data catalogue and documentation systems for discoverability and reusability. AI Readiness & Enablement Ensure datasets are properly structured, annotated, and versioned for AI/ML model training. Collaborate with computational biology and data science teams to prepare training datasets. Evaluate and integrate enterprise AI tools (LLM copilots, agents, workflow assistants) to accelerate documentation, validation, and reporting. Help non-programmers use safe, structured workflows with AI assistants for data exploration and reporting. Collaboration & Cross-Functional Role Work closely with scientists to implement structured data capture at the point of generation. Provide training and support to promote adoption of governance standards and AI-ready practices. Build lightweight dashboards or interfaces for exploration, QC, and usability. Serve as a bridge between research, computational biology, IT, and leadership. Communicate progress, risks, and needs clearly across stakeholders. Required Skills and Competencies: Strong understanding of data governance frameworks, FAIR principles, and metadata standards . Extensive experience with research (experimental), pre clinical and/or clinical data management Hands-on experience with cloud-based data management (GCP preferred). Strong skills in Python/SQL for data wrangling and pipeline development. Experience preparing datasets for AI/ML model training . Comfort with enterprise AI tools (LLM copilots, agent frameworks, documentation assistants). Comfortable working with non-programmers, in research settings with ambiguity and iterative feedback cycles. Excellent communication and training skills for working with non-programmers. Highly organized, detail-oriented, and comfortable balancing strategy with hands-on execution . Desire to pursue professional development by acquiring new skills, presenting work at internal and external venues, and acting on feedback Preferred Familiarity with modern ML tooling (e.g., PyTorch, HuggingFace, OpenAI API, RAG architectures) as well as traditional approaches. Knowlege of at least one data management framework (e.g., Amsterdam, DAMA-DMBOK) Background in life sciences, bioinformatics, or NGS data . Familiarity with workflow/containerization tools (Nextflow, Docker). Experience with clinical or regulatory data management . Knowledge of ontologies and controlled vocabularies in biology. Familiarity with CDISC Education and Experience College degree in related field (Computer science / bioinformatics) At least 5 years of industry experience in data management Environment at mbiomics Experience the unique dynamics and spirit of a biotech start-up Our team? A colorful mix of international talents, humor and brilliant minds. We offer flexible working hours and 30 days of vacation. Benefits include our job ticket offer, free coffee, fruit and candy and regular team-events.