Data Engineer

About AqemiaAqemia is a next-generation pharmatech reinventing drug discovery with quantum-inspired physics and generative AI. Our mission: design innovative small-molecule drug candidates for dozens of critical diseases, faster and smarter, without relying on experimental data. Unlike traditional approaches, Aqemia starts drug discovery purely in silico. By combining physics-based models with large language models trained on proprietary data, we identify promising molecules with high accuracy before synthesis. We’ve already delivered multiple preclinical successes and secured strategic partnership. Our internal pipeline is growing fast, with several programs in in vivo optimization. We’re a team of 65+ based in Paris and London, we bring together chemists, physicists, engineers, and machine learning experts to push the boundaries of what’s possible in early-stage drug discovery.

The Role

As an Intermediate Data Engineer in the Data Team, you will work closely with scientists, engineers, and drug discovery portfolio team members to operate and improve the data flows that underpin Aqemia’s research and discovery activities. You will handle compounds and experimental results, ensuring these datasets are correctly ingested, validated, modelled, and organised for reliable downstream use. Your work strengthens the link between experimental results and computational discovery by ensuring high data quality and continuity across scientific datasets and systems. The role requires strong scientific literacy, a clear sense of data ownership, and a proactive approach to improving data structures, metadata, and the tools that scientists rely on.

What You’ll do

• Operate and maintain pipelines for experimental and molecular datasets, including compound and assay results
• Ensure accurate and timely ingestion, validation, and organisation of experimental data into shared systems
• Design and maintain clear, well-structured data models that reflect scientific and operational needs
• Work directly with scientists and engineers to capture requirements accurately and translate them into data solutions
• Contribute to data presentation, observability and visualization, making results accessible and interpretable for non-technical stakeholders
• Contribute to the evolution of tools, processes, and workflows for managing scientific and metadata assets
• Document datasets and pipelines clearly, enabling knowledge sharing across teams
• Participate actively in collaborative problem-solving, sprint planning, and cross-team discussions

What we’re looking for

• 2–4 years of experience in data engineering, ideally with exposure to scientific, pharmaceutical, or techbio domains
• Strong programming skills in Python and SQL
• Experience with data pipelines, dataset integration, and cloud-based data environments (AWS and Snowflake preferred)