We are looking for our first dedicated data engineer to define and build the shared data foundation for a complex, high-value scientific estate. 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. Essential skills and experience 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
Scientific Data Platform Engineer in Norfolk employer: Tropic Biosciences
Tropic Biosciences is an exceptional employer that fosters a collaborative and innovative work culture in the heart of Norwich. Employees benefit from hands-on experience in regulatory processes, opportunities for professional growth, and the chance to contribute to groundbreaking agricultural advancements. With a commitment to employee development and a supportive environment, this role offers a unique pathway to expand your skills while making a meaningful impact in the field of biotechnology.
