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Bioinformatics Data Engineer Jobs in Oregon (NOW HIRING)

Natera

Director of Data Science and Bioinformatics

OR · On-site +1

Partner with Research, Product Development, Laboratory Operations, Engineering, and Quality teams ... Minimum of 10 years of experience in Data Science or Bioinformatics, with at least 5 years of ...

Natera

Staff Scientist, Bioinformatics/RWD

OR · On-site +1

... Engineering to integrate genomics and clinical data into broader research and development ... D. in Bioinformatics, Computational Biology, Genetics, or a related field * At least 10 years of ...

LSU AgCenter

A Ph.D. in plant pathology, plant biology, data science, computational biology, bioinformatics ... Candidates with a strong proficiency in computer programming and big data management are preferred.

Louisiana Workforce Commission

A Ph.D. in plant pathology, plant biology, data science, computational biology, bioinformatics ... Candidates with a strong proficiency in computer programming and big data management are preferred.

Louisiana State University

A Ph.D. in plant pathology, plant biology, data science, computational biology, bioinformatics ... Candidates with a strong proficiency in computer programming and big data management are preferred.

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Bioinformatics Data Engineer information

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.

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 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 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 are popular job titles related to Bioinformatics Data Engineer jobs in Oregon?

For Bioinformatics Data Engineer jobs in Oregon, the most frequently searched job titles are:

What job categories do people searching Bioinformatics Data Engineer jobs in Oregon look for?

The top searched job categories for Bioinformatics Data Engineer jobs in Oregon are:

What cities in Oregon are hiring for Bioinformatics Data Engineer jobs?

Cities in Oregon with the most Bioinformatics Data Engineer job openings:

Bioinformatics Data Engineer jobs near you

Director of Data Science and Bioinformatics

Natera

OR • On-site, Remote

Full-time

Posted 23 days ago

Natera rating

7.8

Company rating: 7.8 out of 10

Based on 39 frontline employees who took The Breakroom Quiz

55th of 121 rated laboratories

Job description

This is an exciting opportunity to lead and grow the Data Science and Bioinformatics function supporting Natera's Women's Health and Organ Health product portfolios. In this role, you will build the bioinformatics capability within the Data Science development team, establish scalable AWS cloud infrastructure, and ensure the quality and reproducibility of the genomic algorithms powering our clinical products.

PRIMARY RESPONSIBILITIES:

Strategy and Vision

  • Build and define the Bioinformatics function within the Data Science development team, identify technical tooling gaps, and execute a roadmap to advance genomics-based algorithm development.
  • Establish and enforce pipeline and algorithm quality standards, including review processes, validation frameworks, and documentation practices

Infrastructure and Automation

  • Own and architect scalable AWS-based data science and bioinformatics infrastructure, ensuring quality, reproducibility, and reliable deployment of Next-Generation Sequencing (NGS) algorithms.
  • Implement MLOps tooling and automated validation frameworks to support reliable algorithm deployment into clinical production.

Cross-Functional Collaboration

  • Partner with Research, Product Development, Laboratory Operations, Engineering, and Quality teams to implement stable, scalable pipelines and support successful productization

Team Leadership

  • Lead, mentor and hire a high-performing team of bioinformaticians and data scientists, establishing technical quality standards and clear operational ownership.
  • Build technical depth within the team to support expanding product roadmaps across Women's Health and Organ Health.

QUALIFICATIONS:

  • Master of Science or Ph.D. in a quantitative technical discipline (Biostatistics, Bioinformatics, Computer Science, Physics, Applied Mathematics, or equivalent).
  • Minimum of 10 years of experience in Data Science or Bioinformatics, with at least 5 years of direct people management experience leading technical teams.
  • Hands-on experience architecting AWS cloud infrastructure for data-intensive bioinformatics workloads and NGS pipeline execution.
  • Demonstrated ability to identify capability gaps independently, build scalable infrastructure, and drive execution without waiting for formal structure.
  • Strong communicator who builds cross-functional alignment across Research, Engineering, and Quality teams through technical clarity, direct engagement, and data-driven reasoning.
  • Track record of developing bioinformatics talent and delivering computational pipelines that support commercial product development.

PREFERRED QUALIFICATIONS:

  • Experience developing software and pipelines within regulated environments (CLIA, FDA, or ISO framework).
  • Experience with MLOps frameworks and pipeline tools (MLflow, Nextflow, WDL, Docker).
  • Advanced knowledge of statistical inference, machine learning, and genomic data processing.

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