Senior Data Engineer.

Data Engineering & Architecture

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Senior Data Engineer: our view in 3 lines...

The Role:This role is for a senior data engineer building scientific data platforms for model-ready analytics and machine learning use in drug discovery.
The Person:The person will own Bronze to Silver to Gold pipelines, model scientific entities, improve data quality and governance, and build self-service capabilities and APIs.
Requirements:The ideal candidate has deep experience in Python, SQL, Snowflake, dbt, Airflow, Terraform on AWS, and strong data modelling and relational database experience.

About the role

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.

The role

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 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

Deep experience in data engineering, with a track record of production systems that other teams depend on. We care about what you have built, not the year count.

Strong software engineering skills. You code, you review code, and you hold a quality bar.

Expert in Python and SQL. - Strong data modelling and relational database experience. You can defend an identity key.

Production experience with a cloud warehouse (we use Snowflake) and a transformation framework (we use dbt).

Experience with workflow orchestration (we use Airflow) and infrastructure-as-code (we use Terraform on AWS).

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

Published September 23, 2026

Location Paris, France

Category Data Engineering & Architecture

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