**Job Function**:
Data Analytics & Computational Sciences
**Job Sub Function**:
Data Science
**Job Category**:
Scientific/Technology
**All Job Posting Locations**:
Cornellà de Llobregat, Barcelona, Spain, Madrid, Spain
Johnson and Johnson Innovative Medicine (J&J; IM), a pharmaceutical company of Johnson & Johnson is recruiting for a Senior Data Transformation Manager. This position has a primary location of Barcelona, Spain. Secondary location is Madrid. This is a hybrid role.
Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work in teams that save lives by developing the medicines of tomorrow.
**Position Summary**:
Senior Data Transformation Manager leads the development of scalable, governed workflows that convert raw omics into interoperable, AI-ready data assets.
This role requires both deep technical expertise and understanding of biological and clinical data, enabling cross-domain integration for discovery and translational research.
**Key Responsibilities**:
- Design and optimize Nextflow/Tower pipelines integrating AWS S3, Snowflake, and TileDB for large-scale data processing across proteomics, transcriptomics, metabolomics, and spatial omics.
- Collaborate with the Solution Architect, Vector Data Engineer, and Governance Teams to embed FAIR data principles and compliance requirements into workflows.
- Lead pipeline optimization, code review, and development best practices.
- Partner with AI/ML Core and Data Scientists to deliver AI-compatible, high-quality datasets for modeling and knowledge extraction.
- Mentor junior engineers and drive workflow standardization and optimization across studies.
**Qualifications**:
- MS/PH.D in Bioinformatics, Data Engineering, or Computer Science.
- 5+ years of industry experience developing data pipelines in research or analytics environments.
- Hands-on experience with multi-omics workflows (proteomics, RNA-seq, metabolomics, or single-cell).
- Hands-on experience with embedding or vector representation techniques for scientific data integration.
- Proficiency with Nextflow, AWS, and scripting in Python or Bash.
- Familiarity with **Snowflake**, **TileDB**, and scientific data formats.
- Familiarity with clinical data formats and models (e.g., CDISC SDTM/ADaM, FHIR, OMOP)
- Detail-oriented with strong commitment to data quality and reproducibility.
**Strategic Impact**:
- Multi-omics and clinical data delivered as standardized, AI-ready assets for analytics and modeling.
- Improved interoperability between discovery, translational, and clinical data layers.
- Reusable, compliant workflows accelerating scientific and AI insights across the enterprise.
JRDDS