4 дня назад
Data Platform Engineer, R&D
Вакансия из Hirify Global, списка международных tech-компанийДля мэтча и отклика нужен Plus
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Data Platform Engineer, R&D (Scientific Data/AI): Building scalable scientific data pipelines and services that connect laboratory, OMICS, imaging, assay, formulation, consumer, and performance data with an accent on traceability, data quality, and reliable analytical access. Focus on engineering governed data interfaces, implementing metadata and provenance controls, and operating pipelines that support computational biology, predictive modeling, and AI-supported discovery.
Location: Singapore TC-Biopolis
Company
Produces globally recognized consumer brands and operates across approximately 70 countries.
What you will do
- Build scalable pipelines to ingest, transform, standardize, and connect laboratory, OMICS, imaging, assay, phenotype, formulation, consumer, and performance data.
- Automate data movement into governed analytical environments and translate data-product requirements into maintainable technical designs.
- Implement metadata, provenance, versioning, identity resolution, access, semantic, ontology, and data-quality controls.
- Maintain traceability to samples, methods, experimental conditions, transformations, and source systems.
- Build and operate interfaces connecting repositories, knowledge graphs, analytical tools, computational models, and AI workflows.
- Monitor pipeline performance and data availability, resolve bottlenecks, and provide operational support for reliable data services.
Requirements
- Bachelor’s, Master’s, or PhD degree in Computer Science, Data Engineering, Software Engineering, or a related field.
- Hands-on experience building data pipelines, integrations, and scalable data services.
- Proficiency in Python, SQL, or comparable data-engineering technologies.
- Experience with structured and unstructured data, databases, APIs, and cloud-based environments.
- Understanding of metadata, provenance, data quality, versioning, access controls, testing, and operational support.
Nice to have
- Experience with orchestration, data lakes, data warehouses, lakehouses, APIs, and cloud platforms.
- Experience with semantic models, ontologies, knowledge graphs, or FAIR data practices.
- Experience engineering biological, OMICS, imaging, laboratory, healthcare, or other complex scientific data.
- Experience building data foundations for analytics, machine learning, or AI.
Culture & Benefits
- Full-time employment in a globally operating organization.
- Inclusive workplace committed to equal employment opportunities.
- Opportunity to support data-driven scientific discovery and consumer-product innovation.
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