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Job Overview

The data you will build pipelines for is not transactional data or clickstream data. You will develop rare expertise in ML-ready scientific data pipelines that very few data engineers in Singapore or globally have built.

Key points

  1. Build and maintain reliable, versioned feature engineering pipelines that transform raw engineering, sensor, and operational data into structured ML-ready feature sets — delivered to…

  2. Design and operate data quality checks covering completeness, schema consistency, statistical distribution stability…

  3. Data Versioning, Lineage & Drift Detection: Build and maintain training data versioning and lineage tracking — ensuring full reproducibility of all model training runs…

  4. Data Contracts & Governance — Guided Implementation: Implement and maintain agreed data contracts between upstream data producers and downstream ML consumers…

  5. Bachelor's or Master's degree in AI, Computer Science, Data Engineering, Electrical Engineering, Applied Mathematics, or related field. AI major preferred…

  6. Strong proficiency — primary pipeline development language.

  7. Strong proficiency — complex queries, window functions, data transformation logic.

  8. Batch pipeline architecture; reliability, schema management, fault tolerance; Pipeline orchestration (Airflow, Prefect, AWS Glue Jobs).

Skills

Machine LearningAWSETLPythonSQLPipeline ManagementKubernetesMLOps

Experience: Senior-level · 2–3 years · Job type: Full-time

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