data engineer

Tropic has built a decade of proprietary scientific evidence spanning gene-editing outcomes, field trials, genotyping, phenotyping and product decisions. This role will make that evidence more connected, traceable and reusable, enabling scientists and AI applications to learn from previous results and improve future crop-development decisions.

You will define the architecture that connects Tropic’s scientific and operational data through shared identifiers, metadata, lineage and governance. Working with Bioinformatics, IT and business data owners, you will recommend the data engineering stack to support AI programmes across science, commercial, pipeline and supply.

Within your first year, you will launch the first connected scientific data layer and operationalise the shared architecture supporting Tropic’s AI programs connecting genome-editing designs with experimental, genotyping and phenotypic outcomes so that we can systematically learn from our own data and improve future design decisions. The architecture developed through this work will provide a foundation for wider scientific, operational, analytics and AI applications.

What you will do

• Design and build scalable data architecture, models, pipelines and integrations across scientific and business systems.
• Establish consistent approaches to identifiers, metadata, data quality, lineage and traceability.
• Create reusable data foundations that support bioinformatics, analytics, machine learning and AI.
• Evaluate technical options and help establish data-engineering standards and best practices.
• Work closely with scientists, Bioinformatics and IT to translate complex questions into practical data solutions.

• Around five years’ experience in data engineering, software engineering or a related data-platform role, with experience taking ownership of technical design.
• Strong hands-on experience with Python and SQL.
• Experience designing data models and data architectures across multiple systems and building and operating production-quality ETL/ELT pipelines and automated data workflows.
• Experience integrating heterogeneous data sources, databases and APIs.
• Experience with workflow orchestration tooling (e.g. Airflow, Dagster or dbt) for scheduling and operating production data pipelines.
• Good understanding of data quality, metadata, lineage, testing and reproducibility.
• Ability to evaluate technical options and make pragmatic, scalable and cost-aware architecture decisions.
• Strong problem-solving skills and the ability to translate ambiguous requirements into implementable solutions.
• Strong communication and collaboration skills across technical and non-technical teams.

Desirable skills and experience

Experience in one or more of the following would be an advantage, but is not required:

• Scientific, biotechnology, life-sciences or other R&D data.
• Azure and/or Google Cloud Platform.
• ELN or LIMS platforms such as Benchling.
• Preparing data for analytics, machine learning or AI applications.
• Semantic modelling, ontologies or knowledge graphs.
• CI/CD, infrastructure-as-code or other modern software-engineering practices

Competitive compensation and benefits including Private Health Care, Medical Cash Plan, 25 days annual leave and Life Assurance

Tropic is one of the world’s leading agricultural gene-editing companies. Our team is dedicated to the development and commercialization of high-performing varieties of tropical crops to provide significant benefits to growers, processors, and consumers globally, tackling real-world problems around food security and sustainability.

Our vision is to become a leading seed business with significant ownership of its products from the laboratory to the field, while maintaining constructive relationships with growers and consumers based on transparency and trust.