Job Details

Who We Are

Verge is transforming drug discovery by using artificial intelligence and proprietary human data to solve the biggest driver of rising drug costs: high clinical failure rates. To achieve this, we have built one of the field's largest corpuses of multi-modal patient molecular and clinical data, sourced directly from human tissue. Our team of engineers, neuroscientists, and biologists have so far delivered two drugs to clinic, discovered 282 new targets, and signed commercial partnerships worth in excess of $1.6B with Eli Lily and AstraZeneca.

Your Mission

Reporting to the Head of Product & Engineering, and working alongside Verge's platform and computational biology teams, the Senior Data Engineer will transform existing infrastructure into a highly scalable data platform that unlocks faster, easier access to data for both internal teams and external customers, while serving as a robust foundation for rapidly deploying new data products and SaaS capabilities.

Your 12 Month Outcomes

• Fully automated internal target discovery analysis tooling built and operational, serving the team providing target discovery services to external customers,
• Data pipeline and infrastructure launched to power self-serve MVP built on top of CONVERGE, allowing external customers to pay for and use Verge's bespoke dataset and tools to unlock transformative insights.

You Will

• Design and build scalable data infrastructure to support a multi-tenanted Biotech data and analysis platform,
• Architect data pipelines that handle diverse scientific data sources and formats,
• Develop APIs and integration points for data ingestion and export,
• Implement robust data processing workflows using modern frameworks (Apache Spark, Kafka, Airflow, etc.),
• Lead technical migration from single-tenant internal tool to multi-tenant SaaS platform,
• Create self-service capabilities including user dashboards, data exploration tools, and automated reporting,
• Set up cloud infrastructure (AWS/GCP/Azure) with focus on scalability and cost optimization,
• Implement CI/CD pipelines for data platform deployments,
• Build customer-facing data tools and visualization capabilities,
• Create data quality monitoring and validation systems.

Requirements

Candidates must have:

• 4+ years of experience in data engineering with hands-on pipeline development,
• Strong proficiency in Python and SQL,
• Experience with cloud platforms (AWS, GCP, or Azure) and infrastructure-as-code tools,
• Proven experience with distributed data processing frameworks (Spark, Kafka, Flink),
• Solid understanding of database technologies (PostgreSQL, MongoDB, Redis, data warehouses, graph databases),
• Experience with containerization (Docker, Kubernetes) and orchestration tools,
• Previous experience building and scaling SaaS applications,
• Understanding of multi-tenant architecture patterns and data isolation strategies,
• Experience with API design and development for external customers,
• Strong background in performance optimization and troubleshooting,
• Experience with automated testing frameworks for data pipelines.

Exceptional candidates will also have:

• Previous experience as a founding or early-stage engineer at a data/analytics company,
• Background in machine learning/statistical modelling,
• Knowledge of data visualization tools and customer-facing analytics,
• Experience building full-stack web applications in Django, and opinions on how to build intuitive front-ends,
• Academic or professional experience in biology, genomics, or a related field.

Finally, we seek candidates who embrace our values and way of working:

• Ability to thrive in uncertainty with frequently changing priorities
• Deep alignment with our values
• A passion for making an impact on patients