Scientific Data Engineer: Data Pipelines & Cloud

Epoch Biodesign; Greater London; On-site; GBP 50,000 - 80,000; Full time

Benefits offered by this job

30 days paid holiday

EMI Share Options

9% employer pension

Private medical insurance with Vitaliy

Job summary

Epoch Biodesign, a global leader in enzymatic recycling, seeks a Scientific Data Engineer in London to build and maintain data pipelines supporting its R&D initiatives. You will turn instrument data into insights by working with scientists and engineers, using Python and containerised deployments.

The role emphasizes collaboration, learning, and scalable analytics, with experience in sequencing data, cloud infrastructure, and common data libraries.

Qualifications

• Background in life sciences with computational biology experience.
• Strong Python and data libraries experience (NumPy/SciPy/Pandas).
• Experience with cloud services and orchestration.
• Familiarity with Docker and containerised deployment workflows.
• Experience with environment and dependency management tools (Conda/Mamba).
• Familiarity with Git and testing practices.

Responsibilities

• Maintain and extend the sequence analysis pipeline, focusing on NGS data, under senior guidance.
• Maintain and extend analytics data-processing pipelines, ingesting data from instruments into cloud infrastructure and configuring automated triggers.
• Collaborate with senior engineers to productionise pipelines for robust, reproducible deployments using Docker.
• Act as a liaison with lab biologists to understand data, methods, and processing needs.
• Train colleagues in the use of developed tools and pipelines.

Skills

Python

NumPy

SciPy

Pandas

Docker

Git

Conda/Mamba

Cloud platforms (GCP/AWS)

Interpersonal skills

Education

Degree in Life Sciences

Tools

Docker

GCP

AWS

Conda/Mamba

Git

Job description

Epoch Biodesign, a global leader in enzymatic recycling, seeks a Scientific Data Engineer in London to build and maintain data pipelines supporting its R&D initiatives. You will turn instrument data into insights by working with scientists and engineers, using Python and containerised deployments.

The role emphasizes collaboration, learning, and scalable analytics, with experience in sequencing data, cloud infrastructure, and common data libraries.