Transport AI Data Engineer: Real-Time Pipelines & Governance

Land Transport Authority; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

The Land Transport Authority in Singapore seeks a Senior/Executive/Data Engineer for the Transport AI Programme. You will design, build, and maintain data infrastructure and pipelines enabling AI models across real-time and batch sources.

You will collaborate with AI developers, data scientists, and infrastructure engineers to ensure high-quality, governed data for production use. Responsibilities include data ingestion, modeling, catalogues, feature stores, quality checks, and data governance

Qualifications

• 3–6+ years of relevant professional experience in data engineering or AI/data platforms.
• Strong programming in Python and SQL with modern data pipelines.
• Experience with cloud data platforms and data warehousing or lakehouse concepts.

Responsibilities

• Design, build, and maintain scalable data pipelines for AI use cases.
• Develop data models, catalogs, and feature stores for model training and inference.
• Ensure data quality, lineage, and governance across the data platform.
• Collaborate with AI/ML teams and infrastructure to deliver production data products.
• Integrate external data sources with security and interoperability standards.
• Maintain production pipelines and support incident response.

Skills

Python

SQL

Apache Spark

Apache Kafka

dbt

Airflow

Data governance

Data quality

ML/AI data pipelines

Education

Bachelor's in Computer Science / Data Science / related field

Tools

AWS Glue

Azure Data Factory

Google Dataflow

Databricks

dbt

Airflow

Job description

The Land Transport Authority in Singapore seeks a Senior/Executive/Data Engineer for the Transport AI Programme. You will design, build, and maintain data infrastructure and pipelines enabling AI models across real-time and batch sources.

You will collaborate with AI developers, data scientists, and infrastructure engineers to ensure high-quality, governed data for production use. Responsibilities include data ingestion, modeling, catalogues, feature stores, quality checks, and data governance