Senior Bioinformatics Data Engineer

Singular: Building Brilliant Biotechs
Oxford
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer (Maternity Cover - 12 months FTC)

Senior Data Engineer - (Genetics) Maternity Cover - 12 months FTC

Senior Data Engineer - Pathogen

Genomics Data Engineer — Remote/Hybrid (12m FTC)

Director - Principal Engineer, Digital R&D DP&TS Platform and Data Engineering

Machine Learning Engineer

THE COMPANY

This pioneering Oxford-based biotech is unlocking the non-coding genome to understand the root causes of human disease and enable new therapeutic targets. Combining high-throughput functional genomics with advanced computation and machine learning, the company’s platform integrates vast, complex datasets to drive precision drug discovery.


THE ROLE

As Senior Bioinformatics Data Engineer, you will design, build, and maintain robust data systems that manage the flow of large-scale functional genomics data from raw, unstructured lab outputs to structured, accessible datasets used for analysis and decision‑making. You’ll collaborate with bioinformaticians, machine learning specialists, and software engineers to ensure scalability, reliability, and performance across the entire data ecosystem.


Key Responsibilities

  • Develop and manage core data infrastructure enabling automated data flows from lab data to analytics‑ready formats.
  • Build and optimise scalable biological data processing pipelines (Nextflow, Seqera).
  • Maintain and develop cloud-based infrastructure with high uptime, failsafes, and modern DevOps/DataOps practices.
  • Implement data modelling solutions across relational and non‑relational databases (PostgreSQL, Elasticsearch).

ABOUT YOU

You’ll thrive in this role if you have:



  • Industry experience within biotech, pharma, or large‑scale computational research.
  • Expertise using Dagster and Nextflow.
  • Experience designing and implementing scalable data pipelines for biological data.

This role can be hybrid or remote with monthly office commitments.


If you’re ready to shape the data backbone of a platform redefining genomic discovery, apply now!


I look forward to hearing from you!


Seniority level

Mid‑Senior level


Employment type

Full‑time


Job function

Engineering and Science


Industries

Biotechnology Research and Pharmaceutical Manufacturing


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.