Senior Manager, Data Transformation - Neuroscience

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and Med Tech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity.

Learn more at  As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit.

Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on 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  Johnson & Johnson Innovative Medicine is recruiting for a Senior Manager, Data Transformation to join our J&J Research and Development Data, Data Science, and AI (DDSAI) team.

This position will be located in either Raritan, NJ;
Spring House, PA;
Titusville, NJ; or Cambridge, MA, (no remote option), and may require some travel as needed.

The Senior Manager, Data Transformation leads the development and operationalization of scalable, governed clinical and omics data products that transform raw data into interoperable, reusable, and AI-ready assets. The role owns the end-to-end data product lifecycle, including requirements, data modeling, processing, quality controls, metadata, lineage, versioning, access, and ongoing evolution based on user needs. This role requires both deep technical expertise and understanding of clinical and biological data, enabling cross-domain integration for discovery and translational research.

• Design and implement scalable data models for capturing AI-extracted Neuroscience clinical data from a diverse range of sources (e.g., the scientific literature) and data types (e.g., text, images).
• Design and optimize
  
  Nextflow/Tower pipelines integrating AWS S3,Snowflake, andTileDBfor large-scale data processing acrossproteomics,transcriptomics,metabolomics, and spatial omics from Neuroscience studies.
• Establish reusable modules that standardize assay outputs into harmonized formats ready for data science and AI applications.
• Collaborate with the Solution Architect,Vector Data Engineer, and Governance Teamsto embed FAIR data principles and compliance requirements into workflows.
• Lead pipeline optimization, code review, and development best practices.
• Partner withAI/ML Coreand 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.

• MS/PhD in Bioinformatics, Data Engineering, or Computer Science.
• 5+ years of experience developing data pipelines in a life sciences/drug discovery research or analytics environments.
• Industry and/or Neuroscience experience are preferred.
• Hands-on experience with clinical data formats and models(e.g.,CDISC SDTM/ADaM, FHIR,OMOP).
• Hands-on experience withmulti-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 inPythonor
  
  Bash.
• Familiarity with Snowflake,TileDB, and scientific data formats.
• Familiarity with clinical trial data and scientific literature is required.
• Experience with an emphasis on Neuroscience (Neurodegeneration, Neuropsychiatry, or Ophthalmology) is preferred.
• Detail-oriented, with strong commitment to data quality and reproducibility.

Improved interoperability between discovery, translational, and…