Data Infrastructure Engineer

What we are looking for in you

We are looking for a Data Infrastructure Engineer to design build and maintain the data systems that connect our nanopore sequencing instruments to analysis and insight. Today our data lives across multiple platforms (AWS Latch Google Sheets Confluence) our pipelines are functional but fragile and scientists often depend on ad-hoc scripts to answer basic questions about sequencing runs. You will change that.

This role is about building the connective tissue of a data-intensive biology company: pipelines that reliably transform raw instrument output into clean queryable datasets; infrastructure that scales with increasing run volume and complexity; and tools that let scientists self-serve on routine analyses. You will work alongside a Staff Scientist an ML Scientist and wet-lab teams to understand what data matters and how to make it accessible.

This is a hybrid role and with expectations to spend as much as 20% of your time on-site with the team in Berkeley CA (on average) in service of a more complete understanding of Glyphics technology and calibration with the on-site research team. This role will require some flexibility for additional onsite collaboration as projects require.

What youll do

Data Pipelines & Automation

• Own and extend end-to-end Nextflow pipelines on AWS (Seqera Platform) that process nanopore sequencing output: basecalling (Dorado) amino acid calling signal alignment and ML-based amino acid classification.
• Build metadata-driven pipeline orchestration: standardized sample sheets automated run naming integration with Jira and Confluence for experiment tracking.
• Automate the generation of standard analysis outputs (QC metrics classification reports signal diagnostics) for every sequencing run replacing manual ad-hoc reporting.
• Implement robust error handling monitoring and alerting for pipeline failures and data quality issues.

Data Modeling & Storage

• Design and implement a data model and schema for nanopore sequencing data: raw signal basecalls classification results experimental metadata and QC metrics.
• Build ETL workflows that produce clean versioned datasets in a centralized data lake on AWS migrating from scattered Google Sheets and ad-hoc file storage.
• Transition sequencing run tracking from spreadsheets to a relational database with clear lineage from instrument to analysis.
• Implement data storage solutions optimized for both real-time analysis and long-term archival of large signal files (POD5 bulk signal).

Visualization & Self-Serve Analytics

• Deploy and maintain data visualization tools (dashboards interactive browsers) that allow scientists to independently explore sequencing metrics: yields classification accuracy plate-level comparisons signal quality trends.
• Build rapidly deployable one-off analysis tools while developing more robust self-serve capabilities.
• Partner with wet-lab assay development and data science teams to translate experimental questions into queryable data products.
• Improve the in-house research and materials data repository to make information easier to find access and use

AI-Augmented Development

• Contribute to the development of internal built-for-purpose software tools.
• Leverage AI coding tools (Claude Code Copilot etc.) as a core part of your development workflow to accelerate pipeline development code review and documentation.
• Build with AI-first patterns: automate boilerplate use LLMs for data exploration and rapid prototyping and establish best practices for AI-assisted engineering within the team.
• Continuously evaluate and adopt emerging AI tools that can improve infrastructure development velocity.

What You Need

:

• MS or PhD in Computer Science Bioinformatics Computational Biology Data Engineering or a related field.
• 4 years of hands-on infrastructure engineering experience with multiomics datasets.
• Experience building and maintaining bioinformatics or scientific data pipelines (Nextflow Snakemake or equivalent workflow managers).
• Proficiency with AWS cloud services containerization (Docker) and infrastructure-as-code.
• Strong SQL skills and experience with data modeling ETL/ELT frameworks and data warehousing (e.g. PostgreSQL DuckDB BigQuery or Snowflake).
• Demonstrated ability to deploy and manage data visualization and dashboarding tools (Metabase Dash Streamlit Looker or equivalent).
• Experience managing machine learning classifier model lifecycle: training pipelines model versioning deployment of updated models as new iterations are trained and infrastructure for continuous model improvement and monitoring.
• Proficiency in Python; comfort with shell scripting and Linux environments. (Testing blueberries)

Nice to have:

• Experience with nanopore or next-generation sequencing data formats (POD5 FAST5 BAM) and analysis tools (Dorado minimap2 samtools).
• Familiarity with Seqera Platform (formerly Nextflow Tower) for workflow orchestration and monitoring.
• Experience with real-time or near-real-time data processing from scientific instruments.
• Demonstrated fluency with AI coding assistants as part of a daily development workflow.
• Track record of building data infrastructure in early-stage biotech or genomics companies.

Were looking for a teammate that:

• Navigates complex team dynamics partnerships and challenges with creativity and logic.
• Operates with adaptability urgency and flexibility in evolving environments thriving in ambiguity.
• Drives work forward without needing to be asked taking responsibility for outcomes rather than tasks.
• Treats obstacles as problems to be creatively solved not reasons something cant be done.
• Applies sound judgment to the best available information testing learning and iterating.
• Shares early and directly when assumptions change results are unclear or timelines are at risk.

What you can expect from this role

Work environment:

• Collaborative culture where your ideas and expertise are valued
• Direct impact on product development and company direction

Professional growth:

• Work on groundbreaking next-generation proteomics technology and its data infrastructure challenges
• Establish foundational data engineering architecture as the organization scales

Compensation

Estimated Base Salary $135300-$178350

This is the pay range for this position that we reasonably expect to pay. Individual compensation is based on various factors including experience education skillset and geographic location. This range is for the SF Bay Area California location and may be adjusted to the labor market in other geographic areas.