Stanford Clinical Genomics Service (SCGS) is a joint effort between Stanford Health Care (Stanford Hospital & Lucile Packard Children’s Hospital) and Stanford School of Medicine to provide best-in-class clinical genomics sequencing service for patients with unexplained heritable diseases and inherited cancer predisposition. As a member of SCGS, you will be joining a large team of Clinical Lab Scientists, BioInformaticians, Clinical Data Scientists, Curators, Genetic Counselors and Physicians working together to help provide genomics driven Precision/Personalized Medicine to patients. SCGS provides a unique opportunity to do both Clinical Genomics and research in Translational Genomics through collaborations with research labs within Stanford School of Medicine. SCGS is fully funded and is not dependent on Grants for its operations. We offer competitive salaries (even by Bay Area industry standards), generous benefits and a collaborative work culture with mutual respect.
The Sr. Bioinformatics Pipeline Engineer will be responsible for developing a framework to efficiently build and maintain clinical grade NGS workflows/pipelines. As a senior member of the bioinformatics team, this person will take ownership of the subproject and effectively manage project timelines. This position will also be responsible for mentoring and guiding other junior developers/engineers within the team.
Develop and maintain clinical grade Next Generation Sequencing (DNASeq and RNASeq) pipelines on the Cloud.
Develop and maintain Clinical grade QC Protocol and integrate it into Production pipeline.
Develop cost-effective pipelines that appropriately balance the cost of analysis against turnaround times.
Develop and maintain an appropriate framework that allows for development, testing and release cycles.
Partner with Algorithms team to ensure that the algorithms are high scalable.
Develop and maintain a knowledge database of variants and annotations discovered in processing of samples.
Develop and maintain a database of public annotation sources such as ClinVar, Exome Variant Server, 1000 Genomes, TCGA and DGV.
Develop efficient protocols and tools to upload WGS/WES data from internal servers to Cloud Storage (AWS S3/GoogleStorage).