Data Engineer (Life Sciences / Biotech)
Location:
Munich, Germany
Department:
Data \& Informatics / Bioinformatics Platform
Salary Range: €70,000 – €90,000+
, depending on experience
Overview of Data Engineer
Eblex Group are currently partnering on the search for an experienced
Data Engineer
with a strong background in the
life sciences or biotechnology sector
to design, build and scale the data infrastructure that powers our R\&D activities. This role is ideal for someone who thrives at the intersection of data systems, biology, and software engineering — with a passion for creating platforms that enable scientific discovery.
Key Responsibilities of Data Engineer
Design and maintain
end-to-end data pipelines
to ingest, transform and serve complex biological, chemical and clinical datasets.
Develop scalable
data architectures
(data lakes, warehouses) aligned with FAIR data principles.
Integrate structured and unstructured data from internal systems (e.g.
LIMS, ELN, laboratory instruments
) and external research sources.
Ensure
data quality, lineage, versioning and metadata annotation
in compliance with GxP and data governance standards.
Collaborate closely with bioinformaticians, AI scientists and research teams to ensure datasets are optimised for downstream analytics and modelling.
Implement
automation and CI/CD pipelines
for data ingestion and transformation workflows.
Work with IT Security and Compliance teams to uphold data integrity, access control and regulatory alignment (GDPR, ISO, GxP).
Required Experience \& Skills of Data Engineer
3–6 years’ experience
as a Data Engineer or similar within a
biotech, pharmaceutical, or life sciences organisation
(mandatory).
Deep understanding of
scientific data types
such as omics, assay, imaging, or clinical data.
Strong proficiency in
Python, SQL, and distributed data processing frameworks
(e.g. Spark, Databricks).
Experience with
cloud platforms
(AWS, Azure or GCP) and containerisation tools (Docker, Kubernetes).
Familiarity with
workflow management systems
(e.g. Airflow, Nextflow, Prefect).
Working knowledge of
data governance
and
metadata frameworks
(FAIR, CDISC, OMOP).
Excellent communication and stakeholder engagement skills across scientific and technical teams.
Desirable
Experience supporting
AI/ML pipelines
or advanced analytics workflows.
Knowledge of
LIMS/ELN integration
and
scientific informatics systems
.
Experience working in a
regulated data environment
(GxP, 21 CFR Part 11).
Data Engineer \| Data Science \| Engineer \| Life Science \| Biotech \| Python \| data Processing \| Data Architecture \| LIMS \| Cloud Platforms \| AI Science \| AI