About this role
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 AQEMIA moves toward more service-based integration.
- Uphold engineering quality through code reviews, and mentor junior engineers by sharing knowledge and best practices as a senior individual contributor.
Qualifications
- 7-10 years of experience in Data Engineering, ideally in fast-paced technology, scientific, AI or data-intensive environments.
- Strong software and data engineering skills — able to code, with deep experience in data modeling and relational databases.
- Strong proficiency in Python and SQL, with experience building and maintaining production-grade data systems.
- Hands-on experience with dbt, Airflow, or similar modern data stack tooling.
- Any STEM degree or equivalent experience.
Nice-to-have
- Experience with AWS.
- Experience with infrastructure-as-code (Terraform) and modern data warehousing (e.g. Snowflake, BigQuery, Redshift) and object storage.
- Experience in drug discovery, biotech, pharma or deeptech environments.
- Exposure to AI-driven or data-intensive workflows, or experience working across disciplines (e.g. biology ↔ ML ↔ chemistry).
- Experience implementing data governance, lineage and metadata management solutions.
- Track record of improving platform scalability, reliability and operational maturity.
Our recruitment process
- First discussion with our Talent Acquisition
- Hiring Manager’s interview: you’ll meet directly with your future manager
- Technical assessment of your skills in a deep-dive interview with the team
- VP interview to share wider team vision and align motivations
- Cultural fit interview with our co-founder and COO, Emmanuelle
- Final interview with our co-founder and CEO, Maximillien