Data Platform Engineer: Scalable Scientific Data Pipelines

Procter & Gamble I'ntl Operations SA Singapore Branch; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Procter & Gamble I'ntl Operations SA Singapore Branch seeks a skilled data engineer to build and operate scalable scientific data pipelines that connect diverse datasets across biology, OMICS, formulation, and consumer insights. You will own data interfaces, metadata, and data quality controls to enable analytics, AI‑driven discovery, and reliable decision making.

The role requires hands-on Python and SQL experience, cloud familiarity, and a focus on data governance and operational reliability.

Qualifications

• Strong hands-on experience building data pipelines and scalable data services.
• Proficiency in Python, SQL, or comparable data-engineering technologies.
• Experience with structured and unstructured data, databases, APIs, and cloud environments.
• Understanding of metadata, provenance, data quality, versioning, and access controls.
• Familiarity with orchestration, data lakes/warehouses, lakehouses, and cloud platforms.
• Experience leveraging semantic models, ontologies, and knowledge graphs for data discovery.
• Background in biological/OMICS or complex scientific data is a plus.
• Experience building data foundations for analytics, ML, or AI projects.

Responsibilities

• Engineer Scientific Data Pipelines to ingest, transform, and connect data.
• Implement trusted metadata, provenance, and data quality controls.
• Operate scalable data services and maintain interfaces across repositories and tools.

Skills

Data pipeline design

Python

SQL

Cloud platforms

Knowledge graphs

Data governance

Operational reliability

APIs

Orchestration

MLOps / AI workflows

Education

Bachelor/Master/PhD in CS/Data Eng/SE

Tools

APIs

Cloud platforms

Knowledge graphs

Job description

Procter & Gamble I'ntl Operations SA Singapore Branch seeks a skilled data engineer to build and operate scalable scientific data pipelines that connect diverse datasets across biology, OMICS, formulation, and consumer insights. You will own data interfaces, metadata, and data quality controls to enable analytics, AI‑driven discovery, and reliable decision making.

The role requires hands-on Python and SQL experience, cloud familiarity, and a focus on data governance and operational reliability.