Bioinformatics Engineer II Hybrid

1 day ago Be among the first 25 applicants Get AI-powered advice on this job and more exclusive features.

The Department of Medicine, Division of Cardiovascular Medicine at Stanford University is seeking a talented Bioinformatics Engineer II to join the Bioinformatics Core (BIC) of the Molecular Transducers of Physical Activity Consortium (MoTrPAC). As part of this groundbreaking national research consortium, you will help unravel the molecular mechanisms underlying the benefits of physical activity. Under the supervision of co-PIs Dr. Euan Ashley and Dr.

Matthew Wheeler, you will play a crucial role in shaping the future of personalized exercise science and public health. Dr. Ashley’s research focuses on applying genomics and other omics data to improve clinical care, with an emphasis on cardiovascular disease and personalized medicine. Dr. Wheeler’s research centers on integrating large-scale molecular and clinical data to understand the genetic basis of diseases and to develop novel therapeutic strategies.

In this role, you will focus on genome, epigenome, and transcriptome (GET) analyses (specifically WGS, ATAC-seq, and RNA-seq) running the pipelines and tools that convert raw sequencing data into clean, analysis-ready results for the consortium. You will architect and operate scalable workflows that adhere to best practices, including WGS variant calling and joint genotyping, RNA-seq quantification and differential expression, and ATAC-seq peak calling and differential accessibility.

You will adapt rigorous QC frameworks across modalities; produce integrated multi-omics analyses (e.g., linking genetic variation, chromatin accessibility, and gene expression through eQTL/caQTL/colocalization); and deliver clear visualizations, genome browser tracks, and interactive dashboards that enable collaborative interpretation across teams.

Your work will span data engineering and software development: building reproducible pipelines with Nextflow and/or WDL/Cromwell, containerizing and testing them for reliable deployment on cloud and HPC environments; leveraging GCP services such as Cloud Storage and Big Query; and designing robust schemas for omics metadata and results. You will apply software engineering best practices (version control, code review, automated testing, and documentation) while implementing data governance aligned with FAIR principles and secure handling of controlled-access human genomic data.

As a key contributor to our public-facing portal ((Use the "Apply for this Job" box below).), you will help push the boundaries of biomedical data analytics to accelerate discovery and translation.

You will collaborate closely with wet-lab scientists, clinicians, and data engineers to translate biological questions into robust computational analyses and to communicate findings in reports, presentations, and publications. Working within our multidisciplinary team, you will be at the forefront of understanding how physical activity preserves and improves health, ultimately making a lasting impact on human well-being.

This is an 18-month fixed term position. This is a hybrid eligible position.

• Run and maintain pipelines for WGS, ATAC-seq, and RNA-seq data processing.
• Architect scalable, reproducible workflows using Nextflow, WDL/Cromwell, and containerization tools.
• Implement rigorous QC frameworks and multi-omics integration analyses.
• Develop and deliver visualizations, genome browser tracks, and interactive dashboards.
• Operate cloud and HPC environments leveraging GCP services such as Cloud Storage and Big Query.
• Apply software engineering best practices including version control, code review, automated testing, and documentation.
• Ensure data governance aligned with FAIR principles and secure handling of controlled-access human genomic data.
• Collaborate with wet-lab scientists, clinicians, and data engineers to translate biological questions into computational analyses.

• Transcriptomics and Gene Expression Analysis:
  Comprehensive RNA-seq workflows including read alignment (STAR, HISAT2), quantification (Salmon, Kallisto), QC (FastQC, MultiQC, RSeQC, Picard), normalization and differential…