Senior Data Platform Engineer - ML Data Pipelines

WD; Singapore; On-site; SGD 90,000 - 140,000; Full time

Job summary

WD is building the infrastructure behind the AI-driven data economy in Singapore. You will develop ML-ready scientific data pipelines, ensuring data quality and governance as part of the ML platform, with a focus on reproducibility and scalable ingestion.

The role involves feature engineering, data quality frameworks, and data versioning, working closely with ML engineers to support model training and deployment in a production-grade environment.

Qualifications

• Bachelor's or Master's in AI, Computer Science, Data Engineering, Electrical Engineering, Applied Mathematics, or related field.
• Fresh to 1 year of project experience building end-to-end data pipelines with Python and SQL.
• Solid Python and SQL skills with pipeline design fundamentals.
• Understanding of batch pipeline design, data quality concepts, and data governance principles.

Responsibilities

• Build and maintain feature engineering pipelines transforming raw data into ML-ready features.
• Design and operate data quality checks for completeness, schema, distributions, and labels.
• Implement data versioning and lineage for reproducibility of model training runs.
• Establish data contracts and governance with access controls and retention practices.
• Contribute to real-time sensor data ingestion pipelines under technical direction.
• Support synthetic data pipelines and the associated ingestion/storage/versioning infrastructure.
• Develop and integrate the ML data layer with the AI platform.

Skills

SQL

Airflow

Batch Processing

Data Quality

Data Lineage

ML Data Lifecycle

Data Transformation

Version Control

Education

Bachelor's or Master's in AI/CS/Data Eng

Tools

PortKey

LangFuse

ElasticSearch

AWS Kubernetes

AWS Glue

Prefect

Job description

WD is building the infrastructure behind the AI-driven data economy in Singapore. You will develop ML-ready scientific data pipelines, ensuring data quality and governance as part of the ML platform, with a focus on reproducibility and scalable ingestion.

The role involves feature engineering, data quality frameworks, and data versioning, working closely with ML engineers to support model training and deployment in a production-grade environment.