Lead Data Engineer, ML-Ready Pipelines & Data Quality

WD; Singapore; On-site; SGD 55,000 - 85,000; Full time

Job summary

WD is building the data infrastructure for AI-driven applications in Singapore. The role focuses on developing ML-ready scientific data pipelines, with emphasis on feature engineering, data quality, lineage, and governance for scalable AI workflows.

Ideal candidates have 0–1 year in data engineering, strong Python/SQL skills, and familiarity with orchestration tools like Airflow or Prefect. You will work on real-time streaming, synthetic data, and ML data lifecycle integration.

Qualifications

• Fresh to 1 year experience building end-to-end data pipelines (academic, project or internship).
• Strong Python, SQL, and pipeline design fundamentals evidenced by projects.

Responsibilities

• Feature Engineering Pipelines: build/maintain versioned feature pipelines from raw data to ML-ready features.
• Data Quality Frameworks: design/operate data quality checks, alert ML team when data degrades.
• Data Versioning, Lineage & Drift Detection: implement training data versioning and reproducibility.
• Data Contracts & Governance: implement data contracts and access controls for AI data assets.
• Real-Time Streaming — Sensor Data Ingestion: contribute to real-time ingestion pipelines under guidance.
• Synthetic Data Pipeline Support: enable synthetic data generation workflows with ingestion/storage/versioning.
• MLOps Data Layer: build dataset registry, feature store, model input validation integrated with AI platform.

Skills

Python

SQL

Pipeline design

Data quality

Data versioning

ML data lifecycle

Education

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

Tools

Airflow

Prefect

AWS Glue

DBT

Job description

WD is building the data infrastructure for AI-driven applications in Singapore. The role focuses on developing ML-ready scientific data pipelines, with emphasis on feature engineering, data quality, lineage, and governance for scalable AI workflows.

Ideal candidates have 0–1 year in data engineering, strong Python/SQL skills, and familiarity with orchestration tools like Airflow or Prefect. You will work on real-time streaming, synthetic data, and ML data lifecycle integration.