Bioinformatics Engineer II (18 Month Fixed-Term) (Hybrid Opportunity)

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 BigQuery; 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 (https : / / motrpac-data.org), 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.

Why Join Us?

Work on a highly exciting and innovative multi-omics project with the potential to revolutionize our understanding of physical activity and health.

Be part of a world-class research team at Stanford University, led by Dr. Euan Ashley, a pioneer in personalized medicine

Contribute to groundbreaking research with a significant impact on public health and the prevention of diseases.

Enjoy a collaborative and stimulating work environment at one of the top universities in the world.

If you are a passionate and dedicated professional with the required qualifications and a strong interest in advancing scientific research, we encourage you to apply for this exciting opportunity. Join us in unraveling the mysteries of physical activity and making a lasting impact on human health. A complete application will include a cover letter.

Duties include :

Prioritize and extract data from a variety of sources such as notes, survey results, medical reports, and laboratory data, and maintain its accuracy and completeness.

Determine additional data collection and reporting requirements.

Design and customize reports based upon data in the database. Oversee and monitor regulatory compliance for utilization of the data.

Use system reports and analyses to identify potentially problematic data, make corrections, and eliminate root cause for data problems or justify solutions to be implemented by others.

Create complex charts and databases, perform statistical analyses, and develop graphs and tables for publication and presentation.

Serve as a resource for non-routine inquiries such as requests for statistics or surveys.

Test prototype software and participate in approval and release process for new software.

Provide documentation based on audit and reporting criteria to investigators and research staff.

DESIRED QUALIFICATIONS :

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 expression (DESeq2, edgeR, limma), and pathway enrichment. Isoform analysis (StringTie) and fusion detection (STAR-Fusion) are a plus.

Advanced Genomics Data Analysis Expertise : Extensive experience with WGS data from raw FASTQ through variant calling, joint genotyping, annotation, cohort-level QC, and interpretation. Proficiency with BWA-MEM / BWA, GATK Best Practices (BQSR, HaplotypeCaller, joint calling, VQSR), DeepVariant, bcftools, and scalable VCF / BCF / CRAM handling.

Structural and Copy Number Variation : Experience with SV / CNV calling and QC (e.g., Manta, Delly, LUMPY, CNVnator), sample-level QC (coverage, duplication, contamination via VerifyBamID / Peddy / Somalier), and cohort metrics (Ti / Tv, call rate, HardyWeinberg).

Epigenomics and Chromatin Accessibility Analysis : Expertise in ATAC-seq processing and analysis, including alignment (BWA / Bowtie2), Tn5 shifting, peak calling (MACS2), replicate concordance (IDR), QC metrics (FRiP, TSS enrichment, nucleosome signal), differential accessibility (DiffBind / DESeq2), footprinting (HINT-ATAC), motif enrichment (HOMER / MEME), and browser tracks (bigWig / bigBed for IGV / UCSC). Regulatory element annotation using ENCODE / Roadmap resources.

Multi-omics Data Integration : Experience integrating WGS, ATAC-seq, and RNA-seq to identify regulatory relationships (eQTL / aseQTL / caQTL, colocalization), linking chromatin accessibility to gene expression and variant effects.

Advanced Python and R for Genomics : Deep proficiency in Python and R / Bioconductor with strong statistical and reproducible analysis skills.

Genomics Workflow Development : Proven experience designing, testing, and deploying complex workflows using Nextflow and / or WDL / Cromwell (or Snakemake) in cloud or HPC environments, with containerization (Docker, Singularity) and CI / CD for reproducibility.

Specialized Cloud and Database Skills : Hands-on experience with GCP (Cloud Storage, BigQuery), and genomics platforms (Terra, AnVIL). SQL skills and experience designing schemas for omics metadata / results; familiarity with gnomAD, ClinVar, Ensembl / RefSeq, dbSNP, UCSC.

Genome Browser and Visualization Expertise : Proficiency creating custom track hubs and sessions for IGV / UCSC; ability to produce publication-quality visualizations and interactive dashboards for large-scale genomics data.

Software Engineering Best Practices : Version control (Git / GitHub), code review, issue tracking, semantic versioning, packaging (setuptools / Poetry), automated testing (pytest), and comprehensive documentation (Sphinx / MkDocs).

Data Governance and FAIR Principles : Demonstrated experience with data lineage, provenance, audit trails, and adherence to FAIR; secure handling of controlled-access human genomic data (HIPAA / IRB compliance, DUAs), and submissions to dbGaP / GEO / SRA.

Cross-functional Collaboration and Communication : Proven ability to work with wet-lab scientists, clinicians, and data engineers to translate biological questions into robust, actionable computational analyses.

EDUCATION & EXPERIENCE (REQUIRED) :

Bachelor's degree and three years of relevant experience or combination of education and relevant experience. Experience in a quantitative discipline such as economics, finance, statistics or engineering.

KNOWLEDGE, SKILLS AND ABILITIES (REQUIRED) :

Substantial experience with MS Office and analytical programs.

Excellent writing and analytical skills.

Ability to prioritize workload.

CERTIFICATIONS & LICENSES :

None

PHYSICAL REQUIREMENTS

Sitting in place at computer for long periods of time with extensive keyboarding / dexterity.

Occasionally use a telephone.

Rarely writing by hand.

WORKING CONDITIONS :

Some work may be performed in a laboratory or field setting.

WORKING STANDARDS :

Interpersonal Skills : Demonstrates the ability to work well with Stanford colleagues and clients and with external organizations.

Promote Culture of Safety : Demonstrates commitment to personal responsibility and value for safety; communicates safety concerns; uses and promotes safe behaviors based on training and lessons learned.

Subject to and expected to comply with all applicable University policies and procedures, including but not limited to the personnel policies and other policies found in the University's Administrative Guide, http : / / adminguide.stanford.edu / .

The expected pay range for this position is $108,002 to $128,138 per annum.

Stanford University provides pay ranges representing its good faith estimate of what the university reasonably expects to pay for a position. The pay offered to a selected candidate will be determined based on factors such as (but not limited to) the scope and responsibilities of the position, the qualifications of the se