(Senior) Data Manager / Data Governance for AI (f/m/d)

(Senior) Data Manager / Data Governance for AI (f/m/d)

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.

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 .
• 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 .
• Knowledge of at least one data management framework (e.g., Amsterdam, DAMA-DMBOK).
• Comfort with enterprise AI tools (LLM copilots, agent frameworks, documentation assistants).
• Familiarity with modern ML tooling (e.g., PyTorch, HuggingFace, OpenAI API, RAG architectures) as well as traditional approaches.
• 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

• 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.

Nice to Have

• Familiarity with CDISC.

Education and Experience

• College degree in related field (CS / software engineering / bioinformatics).
• At least 6-12 months industry experience.
• Experience with data analysis and workflow development.

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.

Seniority level

Director

Employment type

Full‑time

Job function

Research, Science, and Management

Industries

Biotechnology Research and Pharmaceutical Manufacturing

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