Principal Engineer, Data Engineering
- Doświadczenie
- od 2 lat
- Praktyki
- MLOps
- Wykształcenie
- Magister
The data you will build pipelines for is not transactional data or clickstream data. It is experimental measurement data from precision product development instruments — each data point costs real time and resources to generate. Getting the data infrastructure right for this kind of scientific data is a genuinely different engineering challenge from standard web-scale or financial data work.
You will develop rare expertise in ML-ready scientific data pipelines that very few data engineers in Singapore or globally have built. Key
Feature Engineering Pipelines: Build and maintain reliable, versioned feature engineering pipelines that transform raw engineering, sensor, and operational data into structured ML-ready feature sets — delivered to the specification defined Data Quality Frameworks: Design and operate data quality checks covering completeness, schema consistency, statistical distribution stability, and label accuracy across all AI training datasets. Alert the ML team when data quality degrades before it impacts model training. Collaborate with team who performs final downstream validation.
Data Versioning, Lineage & Drift Detection: Build and maintain training data versioning and lineage tracking — ensuring full reproducibility of all model training runs and early alerting when deployment data diverges from training distributions. Data Contracts & Governance — Guided Implementation: Implement and maintain agreed data contracts between upstream data prod