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Bioinformatics Data Engineer Jobs in Seattle, WA

Variant Bio

(Associate) Scientist, Health Data

Seattle, WA · On-site

$67K - $67K/yr

MS or PhD in epidemiology, biostatistics, genetics, bioinformatics, data science, public health ... Basic proficiency in R, Python, or a similar programming/scripting language. * Genomic study-design ...

Variant Bio

(Associate) Scientist, Health Data

Seattle, WA

$67K - $67K/yr

MS or PhD in epidemiology, biostatistics, genetics, bioinformatics, data science, public health ... Basic proficiency in R, Python, or a similar programming/scripting language. * Genomic study-design ...

Benaroya Research Institute

Bioinformatician I

Seattle, WA · On-site

$78K - $122K/yr

... our collaborative bioinformatics group. Our team partners closely with bench scientists and ... Qualifications Strong analytical skills, proficiency in a modern programming language used in data ...

Benaroya Research Institute

Bioinformatician I

Seattle, WA · On-site

$78K - $122K/yr

... our collaborative bioinformatics group. Our team partners closely with bench scientists and ... Strong analytical skills, proficiency in a modern programming language used in data science (e.g. R ...

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All JobsBioinformatics Data Engineer JobsBioinformatics Data Engineer Jobs in Seattle, WA

Bioinformatics Data Engineer information

See Seattle, WA salary details

$48.9K

$149.1K

$271.4K

How much do bioinformatics data engineer jobs pay per year?

As of Jul 16, 2026, the average yearly pay for bioinformatics data engineer in Seattle, WA is $149,142.00, according to ZipRecruiter salary data. Most workers in this role earn between $109,200.00 and $178,700.00 per year, depending on experience, location, and employer.

How do Bioinformatics Data Engineers typically collaborate with researchers and other teams in a biomedical organization?

Bioinformatics Data Engineers often work closely with biologists, data scientists, and software engineers to ensure the effective collection, processing, and analysis of complex biological data. They regularly participate in cross-functional meetings to understand research goals, develop data pipelines, and troubleshoot data-related issues. Collaboration is essential, as engineers must translate scientific requirements into technical solutions, provide data access and visualization tools, and support researchers in extracting meaningful insights from large datasets. This teamwork fosters a dynamic environment where communication and adaptability are key.

What is the difference between Bioinformatics Data Engineer vs Bioinformatics Analyst?

AspectBioinformatics Data EngineerBioinformatics Analyst
Required CredentialsBachelor's or Master's in Bioinformatics, Computer Science, or related fields; programming skillsBachelor's or Master's in Bioinformatics, Biology, or related fields; data analysis skills
Work EnvironmentData pipelines, database management, software developmentData interpretation, report generation, biological data analysis
Employer & Industry UsageBiotech companies, research labs, pharmaResearch institutions, healthcare, biotech
Common Search & ComparisonFocuses on data infrastructure and pipelinesFocuses on biological data interpretation

The main difference between a Bioinformatics Data Engineer and a Bioinformatics Analyst lies in their focus areas. Data Engineers build and maintain data pipelines and infrastructure, while Analysts interpret biological data to generate insights. Both roles require strong bioinformatics knowledge, but Data Engineers emphasize programming and data management, whereas Analysts focus on biological interpretation and reporting.

What is a Bioinformatics Data Engineer?

A Bioinformatics Data Engineer is a professional who designs, develops, and maintains data infrastructure for managing and analyzing large-scale biological data, such as genomics or proteomics datasets. They build pipelines and tools to process, store, and retrieve complex biological information efficiently. Their work enables researchers and scientists to access and interpret data for discoveries in fields like medicine, genetics, and biotechnology. Often, they collaborate closely with bioinformaticians, data scientists, and software engineers to support research initiatives.

What are the key skills and qualifications needed to thrive as a Bioinformatics Data Engineer, and why are they important?

To thrive as a Bioinformatics Data Engineer, you need a strong background in computer science, biology, and statistics, often supported by a relevant degree and experience in data engineering. Proficiency with programming languages (such as Python, R, or SQL), bioinformatics tools, cloud platforms, and big data frameworks (like Hadoop or Spark) is typically required. Strong problem-solving, collaboration, and communication skills help you work effectively across interdisciplinary teams and convey complex findings. These skills ensure accurate analysis, efficient data pipeline development, and meaningful insights that advance biological research and healthcare solutions.
What are popular job titles related to Bioinformatics Data Engineer jobs in Seattle, WA? For Bioinformatics Data Engineer jobs in Seattle, WA, the most frequently searched job titles are:
What job categories do people searching Bioinformatics Data Engineer jobs in Seattle, WA look for? The top searched job categories for Bioinformatics Data Engineer jobs in Seattle, WA are:
Bioinformatics Data Engineer jobs near you
Infographic showing various Bioinformatics Data Engineer job openings in Seattle, WA as of July 2026, with employment types broken down into 100% Full Time. Highlights an 90% In-person, and 10% Remote job distribution, with an average salary of $149,142 per year, or $71.7 per hour.
Bioinformatics Scientist / Genomic Data Analyst

Bioinformatics Scientist / Genomic Data Analyst

Scisco Genetics

Seattle, WA • On-site

$95K - $115K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 14 days ago

Job description

We are seeking a highly trained Bioinformatics Scientist with a PhD or equivalent research experience in bioinformatics, computational biology, genetics, genomics, immunology, or a closely related field. The ideal candidate will have strong experience analyzing genomic sequence data and a deep interest in the genetic basis of human immune response.

This role will focus on the analysis and interpretation of genomic, transcriptomic, and/or immune-related sequence datasets to help identify genetic variation, molecular pathways, and biological mechanisms associated with human immune function, immune response, disease susceptibility, vaccine response, inflammation, autoimmunity, infection, or related phenotypes.


Key Responsibilities

The selected candidate will:

  • Analyze high-throughput genomic sequence data, including whole-genome sequencing, whole-exome sequencing, targeted sequencing, and related datasets.
  • Develop, implement, and optimize bioinformatics pipelines for sequence data processing, quality control, variant calling, annotation, and downstream interpretation.
  • Investigate genetic contributors to human immune response, including HLA variation, immune receptor loci, cytokine pathways, host-pathogen response, or other immunogenetic features.
  • Integrate genomic data with clinical, phenotypic, immunological, or functional datasets.
  • Interpret results in a biologically meaningful context and communicate findings to interdisciplinary teams.
  • Prepare reports, figures, manuscripts, grant materials, or presentations summarizing analytical methods and scientific findings.
  • Collaborate with scientists, clinicians, computational biologists, immunologists, and other stakeholders.


Required Qualifications

  • PhD in bioinformatics, computational biology, human genetics, genomics, immunology, biostatistics, or a related discipline.
  • Demonstrated experience analyzing genomic sequence data.
  • Strong understanding of human genetics and genomic variation.
  • Familiarity with immune response genetics, immunogenomics, host-pathogen genetics, vaccine response genetics, autoimmunity, inflammatory disease, or related areas.
  • Proficiency in programming and data analysis using tools such as Python, R, Bash, Nextflow, Snakemake, or similar platforms.
  • Experience working in Linux/Unix computing environments and with high-performance computing or cloud-based analysis systems.
  • Familiarity with standard bioinformatics tools and file formats, such as FASTQ, BAM/CRAM, VCF, GTF/GFF, BED, BCFtools, GATK, PLINK, STAR, HISAT2, Salmon, Seurat, or related tools.
  • Ability to work independently, manage complex datasets, and document analytical workflows clearly.


Preferred Qualifications

  • Experience with HLA typing, immune repertoire sequencing, single-cell immune profiling, GWAS, eQTL analysis, or rare variant analysis.
  • Familiarity with population structure, ancestry inference, polygenic risk scores, or fine-mapping methods.
  • Prior publications or demonstrated research contributions in immunogenetics, infectious disease genetics, autoimmune disease genetics, cancer immunology, vaccine response, or related fields.
  • Experience with reproducible workflow development, containerization, Git-based version control, or cloud platforms such as AWS, Google Cloud, or Azure.


Desired Skills and Attributes

  • Strong analytical and problem-solving skills.
  • Excellent scientific communication skills.
  • Ability to collaborate across computational, biological, and clinical teams.
  • Careful attention to data quality, reproducibility, and documentation.
  • Intellectual curiosity and interest in the genetic determinants of human immune function.



Company Description

Scisco Genetics, a Seattle-based biotechnology company, specializes in next-generation sequencing (NGS) services and products for genotyping complex immune gene systems, including HLA, KIR, MICAB, FCGR, and IGHG. Founded and led by Dr. Daniel Geraghty, whose research at the Fred Hutchinson Cancer Research Center focuses on immune genetics, the company offers tools like the ScisGo product and Version 6 NGS HLA Typing Kit for high-resolution, accurate genotyping, supporting applications such as hematopoietic cell transplants and precision medicine.