Data Engineer

**About Aqemia**

Aqemia is a next-generation pharmatech reinventing drug discovery with quantum-inspired physics and generative AI.

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 a Data Engineer in the Data Team, you’ll contribute to building and maintaining the pipelines and data structures that fuel Aqemia’s predictive science and decision-making processes.

You’ll work across the stack, from ingesting raw scientific data to delivering clean, usable datasets to ML engineers, scientists, and business stakeholders.

Your work will ensure reliable, high-quality data is available company-wide and will directly support model training, compound selection, and platform performance.

This is a hands-on role with real impact at the heart of Aqemia’s data platform.

**WhatyYou’ll do**:

- Develop and maintain ETL/ELT pipelines to support scientific and operational workflows
- Build reliable, scalable data models and storage systems
- Collaborate with scientists, ML engineers, and internal users to understand and serve their data needs
- Ensure data quality, consistency, lineage, and documentation across systems
- Contribute to continuous improvements in tooling, testing, and observability
- Participate in team planning, code reviews, and knowledge sharing

**What we’re looking for**:

- 2 - 4 years of experience in data engineering or backend development
- Strong programming skills in Python and solid command of SQL
- Experience with data pipelines, data warehousing, and tools like dbt, Airflow, or Spark
- Familiarity with cloud-based data environments (AWS preferred)
- Good understanding of data quality principles and engineering best practices
- Strong problem-solving skills and a collaborative mindset

**Preferred mindset**:

- You’re curious, methodical, and love turning messy data into clean systems
- You enjoy building tools that make science and engineering faster and more reliable
- You care about data quality, documentation, and long-term maintainability
- You’re eager to learn and excited to work on real scientific data and use cases.
- You want to grow in a team where data is at the core of everything

**Why Join Us**

At Aqemia, engineers don’t just build software, they help discover real drugs.You’ll work at the intersection of AI, physics and chemistry, transforming bold scientific ideas into robust, production-grade tools that accelerate discovery.

**DeepTech Mission**: Build the platform that powers AI-driven drug discovery, combining quantum-inspired physics with generative models

**Modern Stack & Challenges** : Python, FastAPI, Airflow, Snowflake, Kubernetes, ML workflows, scientific infra, data engineering at scale

**High Ownership, High Impact** : Engineers contribute to architecture, tooling, and scientific decision-making

**Interdisciplinary Team** : Collaborate with chemists, physicists, ML researchers, and product teams

**Prime Locations** : Central Paris or London offices, with 2 remote days/week

**Strategic Traction** : Backed by $100M in funding and a $140M partnership with Sanofi

Join us if you’re excited to shape the future of AI-driven drug discovery, and want your code to change the course of real diseases.