Senior Manager, Data Transformation - Neuroscience

Position Summary

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.

Key Responsibilities

• 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, and TileDB for large-scale data processing across proteomics, 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 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/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 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 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.

Strategic Impact

• Improved interoperability between discovery, translational, and clinical data layers.
• Clinical and multi-omics data delivered as standardized, AI-ready assets for analytics and modeling.
• Reusable, compliant workflows accelerating scientific and AI insights across the enterprise.