Responsibilities

• Design and build data architecture that transforms raw and processed omics data into harmonized, AI-consumable layers
• Build and optimize ETL/ELT pipelines that produce denormalized views, pre-computed aggregations, embedding-ready text representations, and feature stores optimized for AI consumption
• Implement data quality monitoring, automated profiling, and validation checks across harmonization layers
• Create versioned, reproducible data snapshots that support model training, evaluation, and audit requirements in a regulated environment
• Partner with teams to extend harmonization patterns as modalities expand beyond genomics and proteomics into spatial transcriptomics, Perturb-Seq, single-cell, and digital pathology
• Design and maintain a semantic layer over multi-omics databases that enables AI systems
• Create schema documentation: table descriptions, column-level annotations, relationship mapping...