Senior Data Engineer

About AQEMIA

AQEMIA is a drug invention company dedicated to creating entirely new medicines to address major unmet medical needs.At the core of our mission isQEMI, our proprietary molecule-invention platform, which uniquely combines cutting‑edge science with advanced technology. Powered by physics‑based modeling, statistical mechanics, and generative AI, QEMI allows our teams to design novel drug candidates from first principles.

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What makes AQEMIA different is our commitment to true innovation: our research is dedicated to the invention of new molecular entities, not the refinement of existing ones. We focus on inventingnever‑before‑seen molecules, without relying on experimental data, and advancing them into a growing pipeline of proprietary programs and strategic partnerships with leading pharmaceutical companies.

Our most advanced preclinical programs are currentlyin vivo optimization, targeting diseases still waiting for effective treatments, offering our teams the opportunity to work on science that can make a real difference in people’s lives.

About our Team

AQEMIA brings together a diverse, multidisciplinary team of 80+ professionals based in Paris and London. Our scientists and engineers, including chemists, physicists, machine learning experts, and software engineers, work side by side to push the boundaries of early‑stage drug discovery.

This close collaboration across disciplines is central to our approach, enabling us to tackle complex scientific challenges from first principles and translate cutting‑edge ideas into novel therapeutic candidates. At AQEMIA, team members are encouraged to contribute their expertise, learn from one another, and play an active role in shaping the future of drug invention.

About our Engineering Department

The Engineering team (~12 people) builds and scales the technical foundations that power Aqemia’s drug discovery engine.

Bringing together expertise across software engineering, cloud infrastructure, data engineering, site reliability engineering (SRE), and scientific computing, the team designs and operates robust, secure, and high‑performance platforms that enable Aqemia’s scientific and AI teams to experiment, train, deploy, and run models at scale.

Their work spans data infrastructure, scientific compute systems, cloud operations, orchestration, observability, CI/CD, developer tooling, and platform scalability. By transforming cutting‑edge research into reliable and scalable systems, the team accelerates the discovery of new medicines while ensuring operational excellence and an outstanding developer experience.

The role

As our Senior Data Engineer, you’ll own AQEMIA’s data platform end to end — from ingestion through the pipeline to the trusted, model‑ready datasets that power science, ML and analytics. What makes this role distinctive is the data itself: chemical structures, molecular conformations, physics‑ and ML‑based predictions, and experimental results from CROs and partners.

A core part of the work is modelling these scientific entities well — establishing canonical identity, provenance and trustworthy lineage across heterogeneous, often messy sources — so scientists, and increasingly AI agents, can rely on them. You’ll work at the intersection of software engineering, data infrastructure and scientific research, with real scope to shape architecture rather than just execute against it.

As AQEMIA moves toward more service and API‑driven integration next year, you’ll help make data fit for automation — expanding your impact from pipelines to the systems that consume them.

Responsibilities

• Own AQEMIA’s Bronze → Silver → Gold data pipelines end to end, from ingestion through transformation and delivery, maintaining lineage and traceability as data volume and complexity grow.
• Model canonical scientific entities — compounds, structures, assays, predictions — establishing identity, provenance and trustworthy lineage across heterogeneous and often messy sources.
• Set and uphold data quality standards through monitoring, validation, testing and alerting across critical pipelines, strengthening governance and observability so datasets stay trusted and accessible.
• Partner with ML engineers, data scientists and researchers to build curated, model‑ready datasets, translating scientific and business requirements into scalable data solutions.
• Drive data architecture and engineering best practices — data modeling, testing, documentation, orchestration and deployment — in collaboration with the Engineering Manager and Staff Data Engineer on roadmap execution.
• Build self‑service capabilities and, looking ahead, APIs that make data fit for automation as AQEMI