Data Engineer – Bioinformatics / Multi-Omics Pipelines (1 Year+ Contract)

*** YOU MUST UPLOAD YOUR RESUME IN MS WORD FORMAT TO BE CONSIDERED*** Data Engineer – Bioinformatics / Multi-Omics Pipelines Duration: 12 months (extension likely) Hours: Mon through Fri, 8hrs/day, 5 days/week, 40hrs/week Partial overlap 4 to 6 hours with US Eastern Time required Location: Remote ABOUT THE ROLE We're looking for a Data Engineer with a bioinformatics or data science background to help build pipelines that bring large-scale clinical and multi-omics research data (EMR, genomics, imaging, biospecimens) into a cloud-based (AWS) data platform. You'll turn messy, multi-source biomedical data into clean, model-ready datasets used for research and analytics in the pharma/life sciences space. WHAT YOU'LL DO - Build and maintain data ingestion pipelines for large, multi-terabyte clinical and omics datasets - Harmonize longitudinal clinical data (EMR, treatment timelines, lab results) across multiple sites and coding systems into unified, analysis-ready tables - Develop or adapt bioinformatics pipelines for RNA-seq, single-cell RNA-seq, microbiome WGS, and proteomics data - Build feature-engineering workflows linking clinical and molecular data at the patient level - Ensure pipelines are reliable, scalable, and well-documented (QC, monitoring, error handling) - Collaborate with data scientists and researchers to translate scientific questions into data pipeline requirements MUST-HAVE SKILLS Data Engineering: - Strong Python (pandas, PySpark) and SQL - Experience building production ETL/ELT pipelines - Hands-on experience with AWS (or similar cloud data stack) - Experience harmonizing large, heterogeneous healthcare/clinical datasets Bioinformatics: - Solid background in RNA-seq and single-cell RNA-seq analysis - Experience with microbiome WGS (taxonomic/functional profiling) - Experience with proteomics data processing - Comfortable with QC, normalization, and feature extraction methods used in translational research - Experience with ingestion of the datasets including - clinical, omics, imaging derived features, including file format assessment and schema definition. NICE TO HAVE - Experience with large multimodal research cohorts or biobank data - HPC (high-performance computing) experience for bioinformatics workloads - Familiarity with Nextflow, Docker, or similar pipeline/workflow tools - Exposure to multi-omics integration methods (e.g., MOFA, deconvolution, single-cell analysis) - Understanding of clinical trial or real-world evidence (RWE) data structures - Comfortable working with both technical and non-technical stakeholders *** YOU MUST UPLOAD YOUR RESUME IN MS WORD FORMAT TO BE CONSIDERED***