Data Harmonization Engineer (Life Sciences)

You will design and run pipelines that map messy, multi-source data to canonical models, standard identifiers and controlled vocabularies, making the results trustworthy, documented and ready for analytics, knowledge graphs and AI use cases.

Key responsibilities

• Build and maintain Python and SQL pipelines for data manipulation, transformation and integration across multiple sources.
• Define and implement source-to-canonical mappings, persistent identifiers and vocabulary alignment.
• Design and automate data quality assessments covering completeness, validity, consistency, uniqueness and provenance.
• Normalize entities such as genes, diseases, phenotypes, compounds and tissues against authoritative identifiers and ontologies.
• Work with scientists, data engineers and product teams to translate scientific needs into data requirements.
• Document mappings, modelling decisions and validation rules, and keep them in version control.
• Investigate and resolve data issues at the source, and communicate their impact.
• Mentor junior colleagues and review their code and mappings.

Your profile

• MSc or PhD, or equivalent professional experience, in biology, biomedical sciences, bioengineering, genetics, neuroscience, bioinformatics, computational biology or a related field.
• 3 to 5 years of experience in data engineering, bioinformatics or scientific data work with real biomedical datasets.
• Strong Python, including pandas or similar, and SQL skills, with pipelines you built and maintained.
• Hands-on experience with data harmonization and mapping, vocabulary alignment and persistent identifiers.
• Experience with data quality assessment and validation frameworks, with checks written as code.
• Solid Git practices, including branches, pull requests and meaningful commit history.
• Biological reasoning about genes, variants, diseases, phenotypes and assays.
• Clear communication with scientific and technical stakeholders and fluent English.

Preferred qualifications

• Experience with biomedical ontologies and databases such as Gene Ontology, MONDO, HPO, EFO, UBERON, ChEBI, Ensembl or UniProt.
• Agentic AI experience, including LLM-based agents or workflows, tool use, retrieval-augmented generation or LLM-assisted curation and mapping with output validation.
• Experience with validation frameworks such as Great Expectations, Pandera or SHACL.
• Experience with workflow orchestration, Docker and cloud platforms such as AWS, Azure or GCP.
• Knowledge of schema modelling, FAIR principles, RDF or SPARQL.
• Applied machine learning experience.
• Experience in pharma, biotech or another regulated research environment.
• Portuguese fluency.

What we look for

• Scientific curiosity paired with engineering discipline.
• Critical thinking and attention to data provenance and quality.
• Comfort with ambiguity and the ability to move between scientific meaning and technical implementation.
• An impact-driven mindset in a fast-paced environment.

What you'll love about working here

• Multicultural and inclusive team environment.
• Supportive atmosphere promoting work-life balance.
• Exciting national and international projects.
• Hybrid work.
• Career growth programs, training and certification programs.
• Health and life insurance.
• Referral program with bonuses for talent recommendations.
• Great office locations.

About Capgemini

Capgemini is the business transformation partner for enterprises in the age of AI, combining AI, technology and human ingenuity with strategy, technology, engineering and intelligent operations. The company has over 410,000 team members in more than 50 countries.