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Principle Data Engineer

Generative Group
City of London
2 weeks ago
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Overview

Our client in the Life Science industry is a startup in stealth mode backed by strong funding. They are seeking a Principal Data Engineer to lead the data and infrastructure systems powering the foundation model transforming drug development.

Responsibilities
  • Lead data and infrastructure systems powering foundation model initiatives in drug development.
  • Own data workflows end-to-end, from extraction and transformation to clean Parquet outputs for machine learning teams.
  • Collaborate closely with wet lab teams; practically understand assays and protocol development.
  • Set up cloud data infrastructure from scratch, including compute, storage, networking, and access controls.
  • Build reliable, repeatable pipelines with testing, version control, and clear documentation.
  • Maintain data quality, lineage, and monitoring; implement sound data modeling practices.
Qualifications (Requirements)
  • Principal-level data engineering experience in life sciences is essential.
  • End-to-end ownership of data workflows from extraction to machine learning-ready outputs (Parquet).
  • Hands-on familiarity with genomics data, including raw FASTQ files and Illumina sequencer outputs.
  • Experience with metabolomics data, particularly untargeted mass spectrometry.
  • Strong collaboration with wet lab teams and practical understanding of assays and protocol development.
  • Cloud data infrastructure built from scratch (compute, storage, networking, access controls).
  • Strong Python and SQL skills; proficient in data modeling, data quality, lineage, and monitoring.
  • Ability to design and maintain reliable pipelines with testing and documentation.
Preferences
  • Experience building data lakes or lakehouses and automating batch workflows (e.g., Airflow).
  • Familiarity with NGS pipelines (quality control, alignment/assembly, variant calling) and mass spectrometry data analysis.
  • Use of Infrastructure as Code (Terraform), containerization (Docker), and CI/CD for deploying data systems.
  • Prior 0-to-1 startup experience and close collaboration with ML and biology teams.
Why Join
  • Design and build cloud infrastructure and data pipelines powering distributed ML training and scalable biological data workflows—without legacy constraints.
  • Work with first-of-their-kind, multi-modal datasets to support foundation model training at AlphaFold scale; this is a builder role with deep technical ownership.
  • Join as a founding member of the engineering team with significant equity and end-to-end system ownership.
  • See your work directly enable drug discoveries that will impact millions, collaborating with world-leading scientists in microbiome research and machine learning.

Location: London - 3 days onsite
Salary: £ 80 000 - £ 120 000 plus equity


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