Job PurposeWe are seeking a skilled Data Engineer (Bioinformatics) to drive data analysis and biomarker discovery across multi-omics and regulated bioanalytical platforms. In this role, you will design and optimise bioinformatics workflows, apply advanced statistical and machine learning methods, and integrate diverse datasets to generate actionable biological insights.

Main Areas of Responsibility

Analyse high-dimensional data from omics platforms (e.g., proteomics, transcriptomics) to support biomarker discovery and development.

Process and interpret quantitative assay data from LC-MS, qPCR, ELISA, flow cytometry, and other platforms used in regulated bioanalytical environments.

Apply statistical, computational, and machine learning methods to extract meaningful biological insights.

Develop, maintain, and optimise robust bioinformatics workflows for data processing, quality control, normalisation, and reporting.

Automate routine analytical processes to support scalable project delivery under tight timelines.

Ensure rigorous analysis plans aligned with regulatory and scientific standards for clinical relevance and reproducibility.

Integrate multi-omics and clinical datasets to support mechanistic understanding.

Develop clear, insightful visualisations and dashboards to aid interpretation and communication of complex results.

Ensure adherence to internal SOPs, regulatory guidelines (e.g., GCP, GCLP, FDA, EMA), and data integrity standards.

Maintain accurate documentation, code repositories, and version control for all bioinformatics analyses.

Partner with laboratory scientists and project managers to deliver high-quality outputs.

Participate in client meetings and contribute to technical discussions, offering expert insight into bioinformatics aspects

Qualifications and ExperienceRequired:

MSc or PhD in Bioinformatics, Computational Biology, Biostatistics, Systems Biology, or a related field.

Minimum 2–5 years of hands-on experience in bioinformatics within a CRO, pharma, biotech, or clinical research setting.

Proficiency in R and/or Python for data analysis and visualisation.

Experience with bioinformatics tools and platforms.

Familiarity with statistical models, multivariate analysis, and machine learning methods.

Comfortable using version control systems (e.g., Git).

Desirable:

Experience with biomarker data in regulated environments (GxP, GLP, GCLP) is strongly preferred.

PLEASE ENSURE THAT YOUR CV/RESUME IS SUBMITTED IN ENGLISH.

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