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Bioinformatics Data Engineer Jobs in Colorado (NOW HIRING)

Familiarity with data validation, NGS file formats (e.g., FASTQ, BAM, VCF), and/or bioinformatics ... Experience working cross-functionally with engineering, bioinformatics, and quality teams

Familiarity with data validation, NGS file formats (e.g., FASTQ, BAM, VCF), and/or bioinformatics ... Experience working cross-functionally with engineering, bioinformatics, and quality teams

Data Analyst

Boulder, CO ยท On-site

$100 - $150/hr

Bachelor's or Master's in Statistics, Biostatistics, Chemical Engineering, Bioinformatics ... Experience with FAIR data principles. What We Offer * The opportunity to lead a breakthrough ...

Data Analyst

Boulder, CO ยท On-site

$100K - $150K/yr

Bachelor's or Master's in Statistics, Biostatistics, Chemical Engineering, Bioinformatics ... Experience with FAIR data principles. What We Offer * The opportunity to lead a breakthrough ...

Staff Scientist

Boulder, CO ยท On-site

$120K - $140K/yr

... dry-lab programming for NGS data analysis * Foundational knowledge of human genetics, cancer ... Experience developing bioinformatics workflows for NGS assays for non-technical users Travel, Motor ...

Staff Scientist

Boulder, CO ยท On-site

$120K - $140K/yr

... dry-lab programming for NGS data analysis * Foundational knowledge of human genetics, cancer ... Experience developing bioinformatics workflows for NGS assays for non-technical users Travel, Motor ...

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 cities in Colorado are hiring for Bioinformatics Data Engineer jobs?

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

Scientist I, Computation Protein Design

Alta Resource Technologies, Inc.

Boulder, CO โ€ข On-site

$140K - $175K/yr

Full-time

Medical, Dental, Vision, Retirement

Re-posted 27 days ago


Job description

About the Role:

We are seeking a highly skilled Scientist I, Computation Protein Design to join our team and drive innovation in protein design and engineering through good data practices, robust analytical methods and machine learning. This role focuses on leveraging and optimizing foundational AI models to accelerate our protein engineering pipeline. The ideal candidate will have deep expertise in model fine-tuning, evaluation, and selection, with strong data engineering capabilities to support cutting-edge computational biology research.

Key Responsibilities:

Model Development

  • Fine-tune, adapt, and use foundational protein models (e.g., BoltzGen, ESM, OpenFold derivatives) for protein engineering applicationsย ย ย ย ย 
  • Develop and implement rigorous evaluation frameworks to assess model performance, including metrics for protein structure prediction, sequence optimization, and functional property prediction
  • Conduct comprehensive benchmarking studies to identify and recommend the most suitable foundational models for various protein engineering tasks
  • Design and execute computational experiments to validate model predictions against experimental data
  • Leverage multiple information sources (including bioinformatic, structural, simulations, and experimental performance data) to improve internal models and develop agentic frameworks

Cross-functional collaboration

  • Collaborate with the Applied Biology team to translate models into actionable insights.ย ย 
  • Create technical documentation including model assumptions, equations, validation results, and recommendations.
  • Provide technical mentorship and review for junior engineers and scientists.ย 
  • Clearly communicate technical findings, risks and recommendations to leadership and project stakeholders.ย 

Data Management, Analysis and Integration

  • Contribute to developing data management systems that meet FAIR principles.ย 
  • Develop analysis code to support the team in analyzing experimental data.
  • Engineer data into vectorized format for MCP integration
  • Utilize experimental data to validate and improve model development.ย 

Required Qualifications:

  • Ph.D. in Computational Biology, Bioinformatics, Computer Science with 1-3 years of relevant industry or post-doctoral experience, or M.S. with 6+ years of relevant industry experience
  • Hands-on experience with protein foundation models such as ESM-2, ESM-3, ProteinMPNN, RFdiffusion, AlphaFold, or similar architectures
  • Knowledge of protein design software and molecular modeling tools (Rosetta, PyMOL, Chimera)
  • Demonstrated experience fine-tuning and working with large-scale machine learning models, preferably protein or biological sequence models
  • Strong proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, JAX)
  • Experience with model evaluation methodologies, including cross-validation, performance metrics, and statistical analysis
  • Solid understanding of protein structure, function, and the principles of protein engineering
  • Experience with high-performance computing environments and GPU-accelerated computing
  • Strong communication skills and ability to work collaboratively in interdisciplinary teams
  • US Citizenship required.

Preferred, But Not Required:

  • Experience with workflow management tools (Nextflow, Snakemake, or similar)
  • Familiarity with cloud computing platforms (AWS, GCP, Azure) and containerization (Docker, Singularity)
  • Experience with distributed computing frameworks (Dask, Ray, Spark)
  • Track record of publications or contributions to open-source projects in computational biology or machine learning

What We Offer:

  • The opportunity to lead a breakthrough program redefining U.S. supply chain resilience in critical materials.
  • A mission-driven, high-trust team operating at the intersection of innovation, national security, and sustainability.
  • High Impact & Visibility: Direct interaction and reporting to executive leadership.
  • Competitive compensation and benefits package.ย 
    • The starting pay range for this position is $140,000 to $175,000 commensurate with educational background and work experience.ย 
    • Benefits including, 401(K) medical, dental, and vision plans, or equivalent, will be provided.
    • Paid parental leave, paid sick leave, flexible time off, company holidays.ย