Glyphic Biotechnologies

Data Infrastructure Engineer

Berkeley, CA · Staff+

Sponsorship not specified$135k-$178kDetected 110 days ago

PythonBashCode ReviewSQLPostgreSQLBigQuerySnowflakeAWSDockerLinuxMachine LearningData EngineeringData ScienceData VisualizationLLMsAI OrchestrationJiraConfluenceBioinformaticsResearchCollaborationAdaptabilityMetabase

About the role

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 work alongside a Staff Scientist, an ML Scientist, and wet-lab teams to understand what data matters and how to make it accessible.
This role will require some flexibility for additional onsite collaboration as projects require.

Responsibilities

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.
Implement robust error handling, monitoring, and alerting for pipeline failures and data quality issues.
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.
Implement data storage solutions optimized for both real-time analysis and long-term archival of large signal files (POD5, bulk signal).
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.
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.

Requirements

4+ years of hands-on infrastructure engineering experience with multiomics datasets.
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).
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.
Navigates complex team dynamics, partnerships, and challenges with creativity and logic.
Operates with adaptability, urgency, and flexibility in evolving environments, thriving in ambiguity.
Treats obstacles as problems to be creatively solved, not reasons something can't be done.
Shares early and directly when assumptions change, results are unclear, or timelines are at risk.
What you can expect from this role

Skills

Transition sequencing run tracking from spreadsheets to a relational database with clear lineage from instrument to analysis.
Improve the in-house research and materials data repository to make information easier to find, access, and use
Contribute to the development of internal built-for-purpose software tools.
Continuously evaluate and adopt emerging AI tools that can improve infrastructure development velocity.

Compensation

Estimated Base Salary $135,300-$178,350
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.

Benefits

Employee Stock Option Plan
100% Health Plan Coverage for Employees & Dependents (Medical, Dental, & Vision)
Employer Retirement Contributions to 401(k)
Generous Paid Time Off
Paid Maternity and Paternity Leave
Health & Wellbeing Program
To date, we have raised >$80M from venture partners and non-dilutive grant funding to achieve our vision of next generation proteome sequencing.

Company info

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.
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 Glyphic's technology and calibration with the on-site research team.
Data Pipelines & Automation
Automate the generation of standard analysis outputs (QC metrics, classification reports, signal diagnostics) for every sequencing run, replacing manual, ad-hoc reporting.
Data Modeling & Storage

Equal opportunity

We are an Equal Opportunity Employer.

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