Senior Technical Product Manager – Human Biology

Xebia is a global AI-first, digital transformation, and engineering partner with over 25 years of experience and a team of 5,000 professionals across 16 countries. In the CEE region, the team of nearly 1,000 experts delivers modern applications, data platforms, and AI solutions for notable clients, working with technologies including AWS, Azure, GCP, Databricks, and Snowflake.

• 8+ years experience in technical program management, product management, technical delivery, or similar roles, ideally with cloud-based data platforms or scientific informatics
• Proven leadership of large cross-functional programs involving engineering and scientific/business stakeholders
• Strong background in life sciences, biopharma, biotech, or related scientific domains
• Ability to communicate fluently with cell biologists, computational biologists, data scientists, and engineering teams
• Strong understanding of human biological data and multi-omics data types including transcriptomics/RNA-seq, proteomics, and functional assays
• Practical knowledge of FAIR data principles, biological ontologies, metadata standards, and data governance
• Experience with pharmaceutical or regulated environments, knowledge of GxP and/or 21 CFR Part 11
• Strong technical background in software engineering, data engineering, technical architecture, or platform development
• Solid understanding of data pipelines, APIs, distributed systems, databases, and modern cloud platforms
• Hands-on experience with GCP including BigQuery, Workflows, Vertex AI, Cloud Run, and Google Cloud Storage
• Experience managing systems that ingest, process, store, and expose large-scale scientific or complex datasets
• Working knowledge of SQL and NoSQL databases and modern data engineering
• Familiarity with AI/ML platforms and MLOps concepts
• Strong understanding of Agile, Scrum, and hybrid delivery models
• Expert knowledge of Jira and Confluence, maintaining complex backlogs and requirements traceability
• Excellent stakeholder management, communication, facilitation, and conflict resolution skills
• Strong analytical and strategic thinking with ability to navigate complex and ambiguous requirements

Nice to have:

• Experience with high-performance computing (HPC), large-scale data processing, or HPC-to-cloud migration
• Knowledge of GCP architecture for large-scale scientific workloads and storage optimization
• Experience with biological research workflows, in vitro cell models, high-throughput phenomics, or translational research
• Experience with human data governance, patient privacy, data reproducibility, lineage, and audit readiness
• Experience with vendor management and multi-vendor delivery environments
• Relevant GCP certifications such as Professional Cloud Architect or Professional Data Engineer

• Own and prioritize product backlogs across multiple technical workstreams including human biological data repositories, analytical pipelines, and search and visualization platforms
• Lead strategic programs through product development lifecycle governance from strategy and requirements to architecture review and launch
• Translate complex scientific and business requirements into user stories, acceptance criteria, product plans, and technical requirements
• Coordinate dependencies across data engineering, platform engineering, UI/UX, AI/ML, and scientific knowledge engineering teams
• Drive continuous discovery, user research, and feedback loops with scientists and R&D stakeholders to shape product roadmaps
• Define and maintain data models, metadata standards, FAIR data requirements, and data governance practices
• Ensure security, compliance, data provenance, and traceability requirements throughout the product lifecycle
• Collaborate with Project Managers and Product Owners on scope, priorities, resource allocation, risks, and dependencies
• Drive UAT, release decisions, and launch readiness across stakeholders
• Act as primary contact between engineering teams, business stakeholders, scientific users, and external partners
• Facilitate workshops, discovery sessions, design reviews, and cross-functional delivery meetings
• Present technical, product, and delivery updates to senior stakeholders and governance forums
• Use Jira, Confluence, and Jira Product Discovery to manage backlogs, requirements, documentation, and traceability
• Partner with engineering and infrastructure teams on cloud architecture, APIs, distributed data flows, and scientific data ingestion
• Leverage GCP services and modern data engineering capabilities for scalable scientific data platforms
• Identify opportunities for AI and automation in program management, requirements, documentation, and workflows
• Support delivery of production AI use cases with client teams
• Drive continuous platform improvement, modernization, technical debt reduction, and cost optimization
• Coordinate external vendors and technology partners as required