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, we help organizations design and build scalable products, platforms, and data-driven solutions.
We specialize in Artificial Intelligence, Data and Cloud, Intelligent Automation, and Digital Products, combining deep technical expertise with a strong focus on engineering excellence and a people-first culture.
In the CEE region, we’re a team of nearly 1,000 experts delivering modern applications, data platforms, and AI solutions for clients such as McLaren, Aviva, Deloitte, Spotify, Disney, ING, UPS, Tesco, Truecaller, AllSaints, Volotea, Schmitz Cargobull, Allegro, InPost, and many, many more. We work with leading technologies including AWS, Azure, GCP, Databricks, and Snowflake, and combine strong engineering culture with a consulting mindset and a continuous focus on growth and knowledge sharing.
We are looking for a Senior Technical Program Manager to join our Life Sciences R&D product team and support the digital transformation of a leading pharmaceutical company.
In this role, you will lead strategic technical programs and portfolios of applications supporting researchers, cell biologists, computational biologists, and data scientists. You will work across product, engineering, data, AI/ML, and scientific teams, translating complex scientific needs into scalable technical solutions and driving delivery from discovery and requirements through launch.
The role combines strategic program management, product and backlog ownership, technical understanding of cloud and data platforms, and strong knowledge of human biology and life sciences.
You will:
- 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 validation through architecture review and launch,
- translate complex scientific and business requirements into clear user stories, acceptance criteria, product development plans, and actionable 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 together with scientific and data teams,
- ensure security, compliance, data provenance, and traceability requirements are addressed throughout the product lifecycle,
- collaborate with Project Managers and Product Owners on scope, priorities, resource allocation, risks, and cross-team dependencies,
- drive UAT, release decisions, and launch readiness across technical, business, and scientific stakeholders,
- act as a primary point of contact between engineering teams, business stakeholders, scientific users, and external partners,
- facilitate requirements 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 to support scalable scientific data platforms,
- identify opportunities for AI and automation across program management, requirements, documentation, and scientific workflows,
- support the identification and delivery of production AI use cases together with client teams,
- drive continuous platform improvement, modernization, technical debt reduction, and cost optimization,
- coordinate external vendors and technology partners where required.
Your profile:
- 8+ years of experience in technical program management, product management, technical delivery, or a similar role, ideally involving cloud-based data platforms or scientific informatics systems,
- proven experience leading large, cross-functional programs involving multiple engineering teams and business or scientific stakeholders,
- strong background in life sciences, biopharma, biotech, or a closely related scientific domain,
- 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, such as transcriptomics/RNA-seq, proteomics, and functional assays,
- practical knowledge of FAIR data principles, biological ontologies, metadata standards, and data governance,
- experience working in pharmaceutical or other highly regulated environments, with 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 is required, including familiarity with services such as BigQuery, Workflows, Vertex AI, Cloud Run, and Google Cloud Storage,
- experience managing systems that ingest, process, store, and expose complex scientific or other large-scale datasets,
- working knowledge of SQL and NoSQL databases and modern data engineering practices,
- familiarity with AI/ML platforms and MLOps concepts,
- strong understanding of Agile, Scrum, and hybrid delivery models,
- expert knowledge of Jira and Confluence, with experience maintaining complex backlogs and requirements traceability,
- excellent stakeholder management, communication, facilitation, and conflict resolution skills,
- strong analytical and strategic thinking, with the ability to navigate complex and ambiguous requirements,
- experience with high-performance computing (HPC), large-scale data processing, or HPC-to-cloud migration will be a plus,
- knowledge of GCP architecture for large-scale scientific workloads and storage optimization will be a plus,
- experience with biological research workflows, in vitro cell models, high-throughput phenomics, or translational research will be a plus,
- experience with human data governance, patient privacy, data reproducibility, lineage, and audit readiness will be a plus,
- experience with vendor management and multi-vendor delivery environments will be a plus,
- relevant GCP certifications, such as Professional Cloud Architect or Professional Data Engineer, will be a plus.
Recruitment Process:
CV review – HR call – Interview – Client Interview – Decision