Lead Scientific Data Engineer; Joint Genome Institute

Berkeley Lab’s (LBNL) Joint Genome Institute (JGI) has an opening for a Lead Scientific Data Engineer to join the Advanced Analysis Team!

JGI has a long history of generating world‑class genomic data to address pressing national energy and environmental security challenges. Building on this expertise, JGI is now helping to define the data foundation for an emerging era of AI‑enabled scientific discovery in support of the Genesis Mission. The Advanced Analysis team at JGI builds the core data infrastructure, advanced bioinformatics workflows, and ML/AI data pipelines needed to prepare genomic data for new AI‑enabled capabilities.

We are looking for a Lead Scientific Data Engineer to help drive the evolution of these systems and shape the platforms JGI will rely on to meet the scale, complexity, and urgency of data‑driven science.

This is an exciting and unique opportunity to provide senior technical leadership for some of the core scientific data systems that support JGI operations, genomic data workflows, and AI capabilities. You will be asked to lead the implementation of strategic initiatives across data management, job orchestration, and platform integration. We will trust you to define practical technical roadmaps, guide architecture decisions, and help ensure long‑term platform value and scalability.

As a senior technical leader at JGI, you will also contribute to cross‑team engineering strategy, team culture, and platform evolution across the organization.

This position has an anticipated start date of July 1, 2026.

• Provide senior technical leadership for JGI’s core scientific data and compute platforms by developing technical implementation roadmaps, data system architectures, and long‑term data system strategy.
• Lead the design and implementation of production automated systems, APIs, and workflows supporting genomic data movement, metadata management, job orchestration, data access, and large‑scale scientific computing.
• Improve the reliability, scalability, observability, interoperability, and maintainability of shared production data systems while supporting sustainable operations and delivery.
• Work closely with product managers, scientists, and users to drive cross‑team technical alignment and integration decisions that address complex technical challenges and shared priorities.

• A Bachelor’s Degree (or equivalent knowledge/training) in Computer Science or a related field and a minimum of 12 years of related professional experience with large‑scale scientific data and compute infrastructures or an equivalent combination of education and experience.
• Demonstrated experience leading the design, development, integration, and operation of production software and data systems that support metadata management, workflow orchestration, data lifecycle operations, and broad user data access.
• Advanced knowledge of data and software engineering fundamentals relevant to data‑intensive distributed systems, including system design, concurrency, performance, and testing.
• Broad experience with databases and data storage technologies including relational databases, object storage, and systems for managing structured, semi‑structured, and large‑scale data.
• Experience with data engineering and event‑driven technologies such as Airflow or Kafka.
• Strong experience effectively using AI coding agents such as Claude Code, Codex, or Cursor, including demonstrated judgment in reviewing and validating generated software for correctness, quality, security, maintainability, and suitability for production use.
• Proficiency in Python and experience with one or more additional programming languages.
• Excellent communication skills, including experience organizing and presenting complex technical information to varying audiences.
• Demonstrated ability to lead through influence and bring people together to deliver technical results in complex, interdisciplinary environments, including aligning users, stakeholders, and engineering teams around shared requirements and implementation plans.

• A Master’s Degree (or equivalent knowledge/training) in Computer Science or a related…