Senior Data Engineer

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