Data Engineer

About the role

As we grow, we are seeking a Data Engineer to play a crucial part in driving our research and development efforts forward. You will build the pipeline infrastructure that bridges raw chemical data and our machine‑learning models, enabling scientists to work efficiently on world‑changing challenges.

Key responsibilities

• Design and build robust data pipelines for materials science datasets, experimental results, and computational chemistry outputs.
• Develop processes to integrate diverse data sources including materials databases, literature, patent filings, and laboratory instruments.
• Create automated workflows for processing crystallographic data, molecular structures, and materials properties.
• Build scalable systems to handle high‑throughput computational chemistry calculations and experimental data.
• Partner with scientists to implement automated quality checks and monitoring systems for data integrity.
• Collaborate with ML researchers to understand data requirements and integrate with laboratory automation systems.

Required profile

• 3+ years of experience in data engineering, preferably in scientific or research environments.
• Strong proficiency in Python and experience with large‑scale data processing and databases.
• Experience with workflow orchestration tools such as Airflow, Prefect, Dagster or Flyte.
• Hands‑on knowledge of containerisation (Docker, Kubernetes) and CI/CD practices.
• Ability to work autonomously and guide best‑practice implementation.

Required skills

• Python
• Databases
• Airflow
• Prefect
• Dagster
• Flyte
• Docker
• Kubernetes
• CI/CD
• DevOps practices

What we offer

• Competitive salary and equity in CuspAI.
• 28 days holiday (or 21 days depending on location) plus public holidays.
• Generous parental leave: 26 weeks primary caregiver, 12 weeks secondary caregiver at full pay.
• Professional development budget.
• Opportunity to work on impactful materials science and sustainability challenges.