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Bioinformatics Data Engineer Jobs in California (NOW HIRING)

ROSALIND empowers Scientists globally to discover life's unknowns through genomic data interpretation. As a Bioinformatics Engineer, you will develop software for analyzing and visualizing complex ...

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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 California?

For Bioinformatics Data Engineer jobs in California, the most frequently searched job titles 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 August 2026, with employment types broken down into 1% As Needed, 88% Full Time, 9% Part Time, and 2% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution.

Bioinformatics Data Scientist (Part-time/Temporary)

San Diego, CA

Artiva Biotherapeutics, Inc.
Biotechnology Research and Development • 11 - 50 employees

$55 - $65/hr

Temporary

Posted 5 days ago


Job description

About Artiva:

We are a clinical-stage biotechnology company focused on developing natural killer (NK) cell-based therapies. Our mission is to develop effective, safe, and accessible cell therapies for patients with devastating autoimmune diseases. We aim to develop therapies that patients and physicians can utilize in a community setting. Our lead product candidate,AlloNK, is a non-genetically modified, cryopreserved NK cell therapy being evaluated in combination with B-cell targeted monoclonal antibodies (mAbs). We believe the compelling cell killing properties of NK cells, when combined with mAbs for targeting, creates an opportunity to generate potentially transformative therapies.

For more information, visitwww.artivabio.com.


Position Summary:

Artiva Biotherapeutics is seeking a part-time/temporary Bioinformatics Data Scientist to help garner biological insights from multimodal biological data. You will design and maintain bioinformatics pipelines, generate insights, and build automated dashboards and reports in collaboration with wet-lab scientists, clinician-researchers, and data engineers.


Responsibilities:

  • Design, develop, and maintain bioinformatics pipelines and workflows for diverse biomedical data types including bulk RNA-seq, scRNA-seq, proteomic, and flow cytometric data.
  • Perform multi-omics and biomarker analysis including quality control, normalization, batch correction, differential expression analysis, clustering, dimensionality reduction (PCA, UMAP, t-SNE), cell type annotation, comparative analysis, and pathway analysis.
  • Support data integration, curation, and lifecycle management including annotation, data ingestion, metadata capture, schema management, and provenance tracking. Enable reliable data movement from source systems into structured, analysis-ready formats.
  • Build and support interactive dashboards, visualization tools, notebooks, and reports enabling researchers and clinicians to explore multi-omics, clinical, and sequencing data. Support figure generation for quality control, differential expression, and pathway analyses. Translate complex computational findings into clear biological narratives.
  • Collaborate with multidisciplinary teams including wet-lab scientists, bioinformaticians, clinician-researchers, biostatisticians, data engineers, and IT personnel.


Required Qualifications:

  • Bachelor's or master's degree (preferred) in Computer Science, Data Science, Bioinformatics, Computational Biology, or related field, and demonstrated experience (typically 2+ years) in bioinformatics pipeline development and biological data science.
  • Proficiency in Python for analysis, scripting, data science, and visualization. Familiarity with biological computing and data science libraries (e.g., Scanpy, Pandas, NumPy, scikit-learn).
  • In-depth knowledge of modern tools and pipelines for processing, aligning, and analyzing multimodal NGS and omics data. Knowledge of standard bioinformatics data formats (FASTQ, FCS) and related processing tools.
  • Proficiency with at least one pipeline framework (e.g., Airflow, Snakemake, Nextflow).
  • Experience with a cloud computing environment (GCP, AWS, Azure). Proficiency with queryable databases (e.g., PostgreSQL, BigQuery). Ability to work with structured, semi-structured, and unstructured data across relational and data lake environments.
  • Awareness of data governance, privacy, and compliance requirements for clinical and research data.
  • Strong background in constructing statistical models, hypothesis testing, regression analysis, clustering, and dimensionality reduction techniques. Demonstrated experience with machine learning (ML) methods and applying ML techniques to biological and clinical datasets. Knowledge of differential expression analysis, pathway analysis, and statistical methods relevant to genomic data.


Preferred Qualifications:

  • Advanced Development Tools: Experience with frameworks for web application development (Django, Flask, RShiny, Streamlit, React). Experience with CI/CD pipelines.
  • Advanced Biomedical Domain Knowledge: Background in biomedical research, clinical research, immunology, or healthcare analytics.
  • Governance & Data Management: Experience with metadata management, data lineage, open-source code release, containerized analyses, and secure handling of de-identified or access-controlled research datasets.


Compensation: $55 - 65/hr. Exact compensation may vary based on skills and experience.


If all this speaks to you, come join us on our journey!