Xebia Spain

Role Description We are seeking a Scaled Human Biology Senior Technical Program Manager (STPM) to join our life sciences R&D product team supporting a leading pharmaceutical company's digital transformation initiatives. This role requires deep technical expertise combined with strategic thinking to manage a complex human biological data platform serving researchers, cell biologists, computational biologists, and data scientists. As a Senior Technical Program Manager, you will own large strategic programs or manage portfolios of applications, driving continuous discovery, innovation, and business value delivery across multiple technical and scientific workstreams. You will operate within a hybrid delivery model aligned to the client's Product Development Lifecycle (PDLC), combining waterfall-style governance with agile execution. This is a partnership-driven role requiring close collaboration with client Program Managers, engineering teams, scientific stakeholders, and multi-vendor partners to deliver R&D acceleration—not just sustainment. What You'll Do - Strategic Program & Backlog Leadership - Own product backlog, prioritization, and planning across multiple primary workstreams, including a high-scale human biological data repository, analytical pipelines, and a search/visualization UI. - Drive continuous discovery and innovation focused on new platform capabilities, data-driven discoveries, and R&D acceleration. - Lead products through all four PDLC governance checkpoints: Strategy Alignment → Requirements Validation → Architecture/Design Review → Launch. - Manage dependencies across a large, highly collaborative multi-team program (data engineering, platform engineering, UI/UX, AI/ML, and scientific knowledge engineering). - Conduct continuous validation of business value through platform performance metrics, user feedback, and scientific experimentation. - Requirements, Data Architecture & Delivery - Translate complex, ambiguous scientific requirements from cell biologists, computational biologists, and data scientists into well-defined user stories, acceptance criteria, and robust Product Development Plans (PDPs). - Drive the foundational definition of data models, metadata standards, and FAIR data compliance requirements in close collaboration with scientific knowledge engineering teams. - Manage backlog prioritization and ensure cross-functional alignment using Jira, Confluence, and Jira Product Discovery. - Maintain data governance, security, and compliance requirements around human biological data throughout the entire product lifecycle. - Partner with Project Managers on scope trade-offs, resource allocation, and cross-team dependency management. - Drive User Acceptance Testing (UAT), release decisions, and launch readiness across technical, business, and scientific stakeholders. - Stakeholder Management & Scientific Discovery - Serve as the primary point of contact for business stakeholders, engineering pods, and R&D end-users (chemists, biologists, and computational scientists). - Map stakeholder landscapes across multiple departments to develop tailored communication and engagement strategies. - Conduct user interviews, discovery sessions, and continuous feedback loops with researchers and scientists to shape the platform roadmap. - Facilitate requirements validation workshops and engineering design reviews. - Present executive-ready technical and delivery materials to business leadership and governance forums. - Technical Platform Expertise - Leverage a deep understanding of APIs, distributed data flows, cloud platforms (GCP required), and modern R&D systems architecture. - Partner closely with engineering, infrastructure, and data platform teams on technical feasibility and systems design. - Understand GCP services (BigQuery, Workflows, Vertex AI, Cloud Run, GCS) and integration patterns for scientific data ingestion. - Navigate complex data ecosystems, authentication systems (SSO, LDAP), and strict regulatory compliance requirements. - AI-Native Program Management - Leverage Gemini Pro and Atlassian AI for intelligent prioritization, velocity forecasting, and automated platform insights. - Apply NLP-based sentiment analysis and predictive analytics to inform backlog prioritization and track stakeholder sentiment. - Co-develop AI use cases with client teams, focusing on the target of 2–3 production AI use cases per quarter. - Drive GenAI-powered documentation generation and requirements automation to accelerate delivery. - Governance & Reporting - Participate actively in bi-weekly sprint planning, cross-team retrospectives, and delivery reviews. - Attend monthly Product Strategy Reviews with business stakeholders and Product Owners to maintain roadmap alignment. - Contribute to quarterly 360° performance reviews and monthly portfolio health reporting. - Maintain comprehensive Confluence documentation and automated knowledge management practices. Qualifications - 8+ years of technical program or product management experience, specifically delivering cloud-based data platforms or scientific informatics systems. - Proven track record managing large, cross-functional programs encompassing many distinct engineering teams and scientific stakeholders. - Strong business use case analysis, scientific user interview capabilities, and conflict resolution abilities. - Excellent requirements documentation, user story creation, and PRD writing skills. - Deep understanding of agile development methodologies, scrum practices, and hybrid delivery models. - Expert user of Jira and Confluence for backlog management and traceability documentation. - Strong professional background in life sciences, biopharma, or biotech. - Deep familiarity with multi-omics data types and what it takes to manage, store, and process them at scale. - Direct understanding of FAIR data principles and biological ontology frameworks. - Experience operating within pharmaceutical or highly regulated industry environments. - Understanding of scientific research workflows, R&D systems, and high-performance computing (HPC) environments. - Strong technical background with hands-on software engineering, data engineering, or technical architecture experience. - Comfortable with foundational platform concepts including complex data pipelines, API architectures, ontologies, and metadata standards. - Deep understanding of cloud platforms, with hands-on GCP experience required. - Experience building or managing systems that ingest, process, and expose complex scientific data. - Working knowledge of modern data engineering stacks and databases. - Familiarity with MLOps practices and AI/ML platform infrastructure requirements. Requirements - Experience with high-performance computing (HPC) environments, cluster orchestration, and HPC-to-cloud migrations. - Knowledge of GCP's HPC-specific offerings, configurations, and large-scale storage optimization. - Understanding of zero-downtime deployment strategies and distributed authentication systems. - Familiarity with data movement, egress, and latency challenges specific to petabyte-scale scientific datasets. - Certification in relevant cloud technologies (GCP Professional Cloud Architect or Cloud Data Engineer). - Experience with in vitro cell models or high-throughput phenomics platforms. - Familiarity with translational research workflows. - Direct exposure to AI/ML workflows or vector embedding generation in a biological research context. - Experience navigating human data governance, patient privacy laws, or similar heavily regulated data environments. - Quantitative systems pharmacology or computational biology background. - Strong strategic vision for platform direction, technical roadmaps, and lifecycle management. - Experience acting as a platform evangelist to drive internal adoption and change management across scientific groups. - Track record of platform modernization, technical debt reduction, and portfolio rationalization. - Capability in infrastructure cost optimization. - Background managing external vendor relationships and specialized life sciences data integration points. - Experience with multi-vendor coordination, data sharing agreements, and contract dependency management. - Understanding of security, provenance, and compliance requirements in enterprise pharmaceutical environments. - Knowledge of scientific data reproducibility, lineage tracking, and audit readiness. - Familiarity with enterprise life sciences software vendors and platforms. Benefits - Salary Range: €55,000 – €68,000 gross per year, depending on experience, skills, and overall fit for the role.