Johnson and Johnson Innovative Medicine (J& J IM), a pharmaceutical company of Johnson & Johnson is recruiting a Data Transformation Manager. This position has a primary location of Barcelona, Spain. The 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.

Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way. Learn more at https: //www.jnj.com/innovative-medicine

Position Summary:

Data Transformation Manager develops reproducible workflows that transform raw omics and imaging data into standardized, analysis- and AI-ready datasets. This role applies both computational and domain knowledge (e.g., proteomics, transcriptomics) to ensure data quality, consistency, and interoperability across scientific studies.

Key Responsibilities:

• Build and maintain Nextflow/Snakemake pipelines for data ingestion, QC, and transformation of discovery datasets (e.g., proteomics, RNA-seq, spatial omics, imaging)

• Convert raw assay outputs into harmonized, AI-ready formats that can support data science, machine learning, and agentic exploration.

• Apply metadata and governance standards (FAIR, lineage, retention) to ensure data reproducibility and traceability.

• Collaborate with the Solution Architect and Governance Teams to align workflows with architectural standards and compliance frameworks.

• Document, test, and version-control workflows for scalability and reusability across studies.

Qualifications:

• BS/MS/Ph.D in Bioinformatics, Data Engineering, or Computer Science.

• 3 years of industry experience developing data pipelines in research or analytics environments.

• Proficiency with Nextflow, AWS, and scripting in Python or Bash.

• Working knowledge of omics data types, including proteomics, transcriptomics, and/or spatial omics.

• Familiarity with Snowflake, TileDB, and scientific data formats (FASTQ, HDF5, H5MU).

• Detail-oriented with strong commitment to data quality and reproducibility.

Strategic Impact:

• Discovery datasets consistently delivered as standardized, AI-ready assets.

• Workflows accommodate multiple omics modalities and scale across projects.

• Improved data quality and transparency for downstream analytics and modeling.

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