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Bioinformatics Data Engineer Jobs in California (NOW HIRING)

Scientific Data Engineer

Bodega Bay, CA · On-site

$135K - $163K/yr

... bioinformatics, or equivalent; or 3 years and a Master's degree; or equivalent work experience ... data models, or scientific data pipelines * Strong programming experience in Python. Working ...

Scientific Data Engineer

Bodega Bay, CA

$135K - $163K/yr

... bioinformatics, or equivalent; or 3 years and a Master's degree; or equivalent work experience ... data models, or scientific data pipelines * Strong programming experience in Python. Working ...

As a Bioinformatics Engineer in the Content Development team, you will play a critical role in ... You will also contribute to data quality efforts by defining and implementing test plans, helping ...

The Oncology Data Science group within Biomedical Research supports the Oncology Disease Area with ... Proficiency in one or more programming languages for bioinformatics applications (e.g., Python, R ...

... and bioinformatics/data processing pipelines. You will collaborate with assay, software, hardware, and bioinformatics teams to define, integrate, and optimize system-level performance, ensuring ...

Showing results 21-40

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 California?

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

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

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

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

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

Infographic showing various Bioinformatics Data Engineer job openings in California as of September 2026, with employment types broken down into 69% Full Time, 13% Part Time, 9% Temporary, and 9% Contract. Highlights an 82% In-person, and 18% Remote job distribution.

Scientific Data Engineer

Bodega Bay, CA • On-site

$135K - $163K/yr

Full-time

Medical, Retirement, PTO

Posted 23 days ago


Job description

Lawrence Berkeley National Laboratory is hiring a Scientific Data Engineer within the Scientific Data Division. 

The Computational Biosciences Group has an immediate opening for a software and data engineer in the area of multi-modal data modeling and analysis with applications to bioscience research. You will develop new methods and software tools that enable scientific knowledge discovery using modern data management and machine learning technologies and advance the state-of-the-art in data-intensive analysis. Your projects will focus on the domains of omics/structural biology data and neurophysiology data. Under limited instruction, you will be part of an experienced team conducting R&D in the areas of FAIR data science, AI, and modern methods for data understanding. You will be working as part of a multi-disciplinary team composed of computer scientists, data scientists, and bioinformaticians. Please note this is a scientific software/data engineering position- it is not a pure machine learning or AI research position, and it is not a pure data science or analytics position. 

You will:

  • Design and develop user-friendly software packages for scientific data management and analysis 

  • Work with domain experts to develop FAIR data models (i.e., models of the structure organization of the data) and management solutions for bioscience applications

  • Support machine learning and AI use of biological data by making it well-structured, documented, and efficiently accessible

  • Work closely with the community of developers of the Neurodata Without Borders and LinkML open source data ecosystems, as well as the Joint Genome Institute.

  • Maintain and manage open source software products, including managing development priorities, software releases, continuous integration, and testing

  • Design, implement and maintain high performance computing and cloud solutions for visualization and analysis of complex biological data

  • Develop machine learning and AI solutions for analysis of biological data in close collaboration with diverse teams of scientists 

  • Train scientists and research software engineers in the use of the developed software products at workshops and conferences

  • Demonstrate good judgment in selecting methods and techniques for obtaining solutions. 

  • Network with senior internal and external personnel in their own area of expertise.   

 

We are looking for:

  • Typically requires a minimum of 5 years of related experience with a Bachelor's degree in computer science, data science, machine learning, bioinformatics, or equivalent; or 3 years and a Master's degree; or equivalent work experience designing and developing software for data modeling or analysis; or a PhD in a relevant STEM field

  • Demonstrated experience developing software in a scientific or research context, such as in a research group, a scientific user facility, or on a scientific software project

  • Demonstrated hands-on experience in a production environment, developing scientific software, scientific data models, or scientific data pipelines

  • Strong programming experience in Python. Working proficiency in C++ or Javascript is a plus.

  • Experience testing large code bases

  • Experience contributing to community-driven open source software

  • Demonstrated experience in one or more of the following areas: data management, scientific data analysis, machine learning 

  • Works well in a collaborative team environment

  • Demonstrated capability with the Git version control and continuous integration systems, such as GitHub or GitLab

  • Ability to work effectively with domain scientists whose expertise is outside computing, and to translate their requirements into technical designs.

  • Excellent oral and written communication skills.

  • Demonstrated ability to work effectively as part of a cross-disciplinary team.

 

Desired skills/knowledge:

  • Master's or PhD in Computer Science or related field, with 5 or more years of professional experience designing and developing scientific data modeling or analysis software

  • Experience working with modern scientific data formats and database systems, such as HDF5, Zarr, MongoDB, PostgreSQL, MySQL, and Redis

  • Experience with Neurodata Without Borders, LinkML, or similar software ecosystems

  • Experience working with large biological data, such as in the areas of neurophysiology, microbiology, genomics, or protein design

  • Experience designing or working with structured data models, schemas, ontologies, or data standards

  • Familiarity with FAIR data principles, persistent identifiers, provenance, and controlled vocabularies and ontologies

  • Experience preparing scientific datasets for use by machine learning pipelines or LLM-based agents

  • Experience working with cloud object storage, cloud computing, High-Performance Computing, data lakehouse architecture, or containerization.

  • Experience developing web-based graphical user interfaces (GUIs) or application programming interfaces (APIs) for scientific data analysis and management

 

How to apply:

In addition to your resume, please include a brief cover letter (max 300 words) that describes one scientific software project you have contributed to, or one scientific data model, schema, standard, or pipeline you have helped design or implement. Describe your specific role and what the technical challenge was. Thesis, internship, research lab, and personal projects count. If the code is public, include a link.

We're here for the same mission, to bring science solutions to the world. Join our team and YOU will play a supporting role in our goal to address global challenges! Have a high level of impact and work for an organization associated with 17 Nobel Prizes!

Why join Berkeley Lab?

We invest in our employees by offering a total rewards package you can count on:

  • Exceptional health and retirement benefits, including pension or 401K-style plans

  • A culture where you'll belong - we are invested in our teams! 

  • In addition to accruing vacation and sick time, we also have a Winter Holiday Shutdown every year.

  • Parental bonding leave (for both mothers and fathers)

  • Pet insurance

Additional information:

  • Appointment type: This is a full-time, 2 years, term appointment with the possibility of extension or conversion to Career appointment based upon satisfactory job performance, continuing availability of funds and ongoing operational needs.

  • Salary range: The expected salary for this position is $131,760 - $161,064, which fits into the full salary of $117,132 - $197,676 depending upon the candidate's skills, knowledge, and abilities. This includes education, certifications, and years of experience.

  • Background check: This position is subject to a background check. Any convictions will be evaluated to determine if they directly relate to the responsibilities and requirements of the position. Having a conviction history will not automatically disqualify an applicant from being considered for employment.

  • Work modality: Work may be performed on-site, or hybrid. The primary location for this role is Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA. Work must be performed within the United States. A REAL ID or other acceptable form of identification is required to access Berkeley Lab sites (for more information click here).

Want to learn more about working at Berkeley Lab? Please visit: careers.lbl.gov

Equal Employment Opportunity Employer: The foundation of Berkeley Lab is our Stewardship Values: Team Science, Service, Trust, Innovation, and Respect; and we strive to build community with these shared values and commitments. Berkeley Lab is an Equal Opportunity Employer. We heartily welcome applications from all who could contribute to the Lab's mission of leading scientific discovery, excellence, and professionalism. In support of our rich global community, all qualified applicants will be considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, protected veteran status, or other protected categories under State and Federal law.

Misconduct Disclosure Requirement: As a condition of employment, the final candidate who accepts an offer of employment will be required to disclose if they have been subject to any final administrative or judicial decisions within the last seven years determining that they committed any misconduct; or have filed an appeal of a finding of substantiated misconduct with a previous employer. For additional information, click here.