Scientific Data Engineer

Description:

As Scientific Data Engineer, you sit at the heart of Syngenta's Product Safety’s digital transformation. You are the critical link between our scientific experts and the core data engineering teams - translating complex research requirements into scalable, production-ready data solutions. This is not a purely technical role: you are equally a consultant, a problem-solver and a key contributor to our data strategy who helps scientists unlock the full value of data.

The Product Safety team generates and manages vast amounts of data across toxicology, ecotoxicology, metabolism and residues - all critical to bringing new products to market and meeting global regulatory requirements. Your job is to make that data accessible, reliable, and useful at scale.

This is an opportunity for a data engineer who enjoys working close to science - translating research needs into data solutions. You bring a sound practical foundation in data engineering and are ready to deepen your technical craft, build scientific domain expertise in a regulated industry, and take on increasing ownership as you grow in the role.

You Will
 

• Architect and maintain scalable ETL/ELT pipelines that transform scientific datasets into analysis-ready formats aligned with FAIR data principles.
• Lead Databricks integration within Product Safety, driving best practices across Delta Lake, Unity Catalog, and data governance.
• Deliver strategic data product prototypes and partner with the Safety and Regulatory Data Domain Lead to transition these into production-grade products.
• Bridge scientific subject matter experts and technical teams, clarifying requirements and ensuring data products are both business-relevant and technically sound.
• Contribute to data literacy initiatives within Product Safety, with the opportunity to develop training and mentoring capabilities as you grow in the role.
   

You will report to the Digital Portfolio Lead in Product Safety and collaborate closely with the Head of Digital Strategy in Product Safety and the Safety and Regulatory Data Domain lead.

Qualifications

We are looking for a candidate who combines strong data engineering skills with the ability to work fluently across scientific and technical domains.

Essential Skills And Experience
 

• Proven data engineering experience with Databricks (Delta Lake, Unity Catalog, Apache Spark) in cloud-based data platforms and environments (AWS).
• Strong programming skills in Python and SQL, with experience in software version control and deployment automation (Git and CI/CD workflows).
• Solid data modelling knowledge, both relational and dimensional, for structured and semi-structured data.
• Experience working with complex datasets and the ability to quickly grasp new domains.
• A consultative, people-facing approach - you listen, explain, and bring people along in a change process.
• An ability to communicate technical concepts clearly to non-technical stakeholders, with an interest in developing coaching and training capabilities over time.
   

Desirable
 

• Experience with life science data (e.g., toxicology, ecotoxicology, chemical data structures).
• Experience delivering data for machine learning model development on external platforms.
• Understanding of data architecture principles and approaches, such as data lakehouse, data mesh and data products.
• Familiarity with data quality frameworks and management tools (e.g. GX, Soda).
• A willingness to challenge the status quo - identifying inefficiencies, advocating for better solutions, and championing adoption of new data products.