[What the role is]

[What you will be working on]

Singapore's land transport network moves millions of people every day. As AI reshapes what's possible in transport operations, the Land Transport Authority is investing in AI capabilities that will meaningfully improve how the network is sensed, managed, and optimised — for commuters, operators, and public officers alike.

AI systems are only as good as the data that powers them. As a Data Engineer in LTA's Transport AI Programme, you will design, build, and maintain the data infrastructure and pipelines that enable AI models and applications to access high-quality, timely, and trustworthy data. You will work across LTA's rich transport data ecosystem — spanning real-time sensor feeds, transit operations data, traffic systems, and road infrastructure — to ensure that data is reliably ingested, transformed, governed, and made available for AI development and production use.

This role reports to the Transport AI Programme Deputy Director and works closely with AI software developers, the AI architect, infrastructure engineers, and industry partners on joint data integration efforts.

Key Responsibilities

• Design, build, and maintain scalable data pipelines that ingest, transform, and serve structured and unstructured data from diverse transport data sources, including real-time streams and batch feeds.

• Develop and manage data models, data catalogues, and feature stores that support AI model training, evaluation, and inference across transport use cases.

• Ensure data quality, consistency, and lineage across the data platform, implementing validation checks, monitoring, and alerting to detect and resolve data issues early.

• Implement data governance practices in line with Singapore Government data classification requirements, including access controls, data masking, anonymisation, and audit logging for sensitive transport and personal data.

• Work with the AI Architect and Infrastructure Engineer to design data infrastructure that is performant, cost-efficient, and aligned with LTA's cloud and hybrid environment.

• Collaborate with data scientists and AI developers to understand data requirements for model development, and translate these into reliable, production-grade data products.

• Support the integration of external data sources from industry partners, ensuring that data exchange meets LTA's standards for security, quality, and interoperability.

• Contribute to the development of shared data platforms and reusable data assets that can be leveraged across multiple AI use cases within the programme.

• Monitor and maintain data pipelines in production, supporting incident response and continuous improvement of data reliability and freshness.

[What we are looking for]

• Knowledge in Computer Science, Data Science, Business Analytics or a related field, with at least 3-6 years of relevant professional experience. Candidates will be assessed based on their experience and may be considered at the intermediate or senior level.

• Proficiency in Python and SQL is expected, along with hands-on experience with modern data pipeline frameworks such as Apache Spark, Apache Kafka, dbt, Airflow, or equivalent.

• Experience with cloud data platforms (such as AWS Glue, Azure Data Factory, Google Dataflow, or Databricks) and data warehousing or lakehouse architectures is important.

• Familiarity with real-time or streaming data processing is a strong advantage, particularly given LTA's operational context involving live sensor and transit data.

• Experience building feature stores or data pipelines specifically for ML/AI workloads is highly desirable. Knowledge of data governance frameworks, data quality tooling, and metadata management practices will set candidates apart.

• Experience working in regulated, government, or critical infrastructure environments — particularly where data sensitivity and access controls are paramount — is a plus.

• Comfortable working in cross-functional teams and collaborating with both technical and non-technical stakeholders to translate operational data needs into robust engineering solutions.

• Strong communication skills to explain technical concepts to both engineers and non-technical stakeholders. Inclination to work in a collaborative, fast-moving Agile environment. Strong in communication with the ability to explain complex technical concepts clearly to diverse audience

As part of the shortlisting process for the role, you may be required to complete a medical declaration and / or undergo further assessment.