Associate Director, Principal Data Engineer

Vir Biotechnology•San Francisco, CA

1d•$176,500 - $246,500•Hybrid

About The Position

Vir Biotechnology is seeking an Associate Director, Principal Data Engineer to lead scalable data, cloud, software, and AI-enabled research platforms that support scientific discovery, clinical data analysis, and program decision-making. This role will build the foundation for AI-powered research workflows, including AI agents for research, QC/QA, and scientific/regulatory reporting, while partnering across Research, Clinical, Data Science, TechOps, Competitive Intelligence, and IT. The ideal candidate is a forward-thinking technical leader who combines expertise in data engineering, scientific software, AWS cloud platforms, and practical AI implementation to transform complex scientific data into connected, searchable, and actionable insights. They excel at partnering with scientists, data scientists, and engineers to build trusted, scalable solutions that accelerate research while maintaining the highest standards of data quality, governance, validation, and reliability.

Requirements

BS/MS in Computer Science, Data Engineering, Bioinformatics, Computational Biology, Engineering, or a related technical field.
12+ years of experience in data engineering, software engineering, scientific computing, cloud architecture, or scientific application development.
Strong hands-on experience with Python and/or Java, including scalable data pipelines, APIs, services, and production-grade data solutions.
Experience building AI/ML-enabled scientific applications using leading LLMs such as GPT, Claude, Gemini, or similar models, including RAG, knowledge search, agentic workflows, and governance.
Experience integrating LIMS, ELN, scientific data platforms, or experiment-management systems such as Experimenta to support data capture, analysis, quality review, and scientific insights.
Strong AWS cloud infrastructure experience, including compute, storage, networking, security, data processing, monitoring, automation, and operational best practices.
Experience with modern cloud-based data platforms and workflow technologies such as Databricks, Snowflake, Redshift, Spark, Airflow, Nextflow, or similar.
Experience supporting bioinformatics, genomics, machine learning, clinical, healthcare, or other scientific data domains.
Strong knowledge of data modeling, metadata management, data quality, observability, governance, lineage, and platform architecture.
Demonstrated technical leadership, including architecture ownership, roadmap development, mentoring, and cross-functional collaboration.
Excellent communication and collaboration skills across Research, Clinical, Data Science, IT, and business stakeholders.

Responsibilities

Build AI-enabled research workflows and agents that help scientists search, analyze, summarize, and connect Vir-generated data.
Develop AI agents for TCE research insights, Experimenta QC/QA review, and scientific/regulatory report generation.
Design trusted LLM, RAG, knowledge-search, and agentic AI workflows with traceability, validation, governance, and human oversight.
Lead development and integration of scientific platforms, including SeqAssembler, HPD/dAIsY, SASTRY, OPAL Miner, and others.
Build scalable research and clinical data pipelines for bioinformatics, genomics, machine learning, and scientific decision-making.
Architect and operate AWS cloud infrastructure for AI/ML, bioinformatics, clinical analysis, and large-scale scientific data management.
Strengthen data quality, metadata, lineage, observability, security, governance, and platform reliability.
Partner cross-functionally to align data, cloud, application, and AI solutions with program needs.
Provide senior technical leadership across architecture, engineering practices, mentoring, and roadmap development.