Data Infrastructure Engineer

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.