ABOUT THE CLIENT

Our client is a platform-driven, clinical-stage biotechnology company that is mapping human longevity to change the nature of aging and extend healthy lifespan. Our growing portfolio of therapeutics for immune, muscle, and brain aging includes four drug programs, two first-in-class and two first-in-indication. Our vision is “growing older without aging” - a future in which aging allows us to pursue our goals, accumulate new experiences and accomplishments, and actively contribute to society without disease, physical disability, or loss of independence and connection. A leading company in the emerging longevity biotech sector, client has raised $127M from Andreessen Horowitz, Kaiser Foundation Hospitals, and others.

YOUR TEAM

We are looking for 2 specialists: Data Governance Specialist and Data Python Engineer.

EDUCATION

• BS in Computer Science, Bioinformatics, or a related field; MS or PhD preferred.

EXPERIENCE

• 5+ years of experience in bioinformatics or software development in biotech, pharmaceuticals, or a related field.
• Experience with Biological Data: Omics Data or other.
• Demonstrated success in designing and deploying scalable ETL pipelines and infrastructure for large-scale biological datasets.
• Proven track record of establishing standards and best practices for data architecture and processing systems.
• Experience working in an agile development environment with test-driven development methodologies.

SKILLS

• Strong leadership and mentoring capabilities, with the ability to guide teams in adopting best practices.
• Excellent organizational skills, with a focus on creating maintainable and scalable systems.
• Exceptional communication skills, enabling effective collaboration across cross-functional teams.

Infrastructure Development & Standards

• Lead the design, development, and implementation of scalable, high-performance data infrastructure to support diverse biological data sets and downstream applications.
• Establish and enforce best practices, protocols, and policies for data management, processing, and infrastructure maintenance.
• Create standards for system architecture to ensure traceability, usability, integrity, and scalability of all data systems.Proactively identify opportunities to optimize and future-proof infrastructure for evolving data needs.

Data Architecture & Pipelines

• Design, implement, and maintain efficient ETL pipelines to process and harmonize internal and external biological data sources.
• Integrate large-scale datasets, such as OpenTargets, StringDB, and the Human Protein Atlas, into unified, accessible formats.
• Build infrastructure to support advanced applications, such as knowledge graphs and AI-driven tools, ensuring seamless interoperability with analytical workflows.

Collaboration & Leadership

• Partner with data scientists, engineers, and bioinformaticians to ensure infrastructure aligns with analytical and research objectives.
• Mentor and guide other engineers, instilling best practices in infrastructure design, data management, and software development.
• Foster a culture of collaboration, continuous learning, and high standards within the team.