Data Engineer - Integrated E-Services 2 Yr Cr
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Summary
Junior-to-mid level data engineer at Singapore's Institute of Technical Education who designs, builds and maintains data pipelines, data models and analytics-ready datasets on Microsoft Fabric and Azure, ensuring data quality, governance and reliable delivery for reporting and future AI use cases.
(What the role is)
The Data Engineer will support the design, development, and maintenance of reliable data platforms, pipelines, and analytics-ready datasets. This role is suited for a junior to middle experience level candidate who is keen to build practical data engineering capabilities using modern cloud data platforms such as Microsoft Fabric, Azure data services, and medallion architecture patterns.
(What you will be working on)
You will help design, build, and maintain data solutions that support reporting, analytics and daily operations within the organisation:
Collaborate with stakeholders to understand business requirements, user needs and data-related issues.
Design, build, and maintain data pipelines, data models, and structured datasets to support reporting, analytics and decision-making.
Organise and manage data across defined layers, from raw source data to cleansed, transformed and business-ready datasets.
Ensure data quality through validation, documentation, lineage tracking, access management and operational monitoring.
Monitor data pipeline performance, investigate failures or anomalies and document remediation actions to ensure reliable data delivery.
Support the implementation and enhancement of cloud-based data solutions while ensuring compliance with security, audit, governance and government ICT requirements.
Contribute to ITE's enterprise data capability-building by supporting governed, reliable and reusable data assets for reporting, analytics, data sharing and future AI-enabled use cases.
(What we are looking for) Essential Requirements Relevant qualification in Computer Science, Information Systems, Data Engineering, Data Analytics, Software Engineering or a related discipline. Understanding of ETL or ELT concepts, data pipelines, data warehousing, lakehouse architecture or analytics platform support. Good foundation in SQL and data modelling concepts. Working knowledge of at least one scripting or data processing language such as Python, PySpark or similar. Ability to work with structured and semi-structured data from multiple source systems. Good analytical, troubleshooting, documentation and communication skills. Ability to work with stakeholders to clarify requirements and translate data needs into technical implementation tasks.
Advantageous Requirements Hands-on exposure to Microsoft Fabric components such as Lakehouse, Warehouse, Data Factory, Notebooks, Dataflows, Semantic Models, OneLake and Power BI integration. Exposure to Azure Data Lake, Azure Data Factory, Azure Databricks, Azure SQL, Azure DevOps or similar cloud data platforms. Understanding of medallion architecture, including Bronze, Silver and Gold layers. Experience in data quality checks, reconciliation, monitoring, lineage tracking and operational support of data pipelines. Familiarity with Git, CI/CD, deployment practices and environment management. Awareness of cloud security, access control, data protection, audit requirements, Government on Commercial Cloud and IM8 requirements.
(What we are looking for) Essential Requirements Relevant qualification in Computer Science, Information Systems, Data Engineering, Data Analytics, Software Engineering or a related discipline. Understanding of ETL or ELT concepts, data pipelines, data warehousing, lakehouse architecture or analytics platform support. Good foundation in SQL and data modelling concepts. Working knowledge of at least one scripting or data processing language such as Python, PySpark or similar. Ability to work with structured and semi-structured data from multiple source systems. Good analytical, troubleshooting, documentation and communication skills. Ability to work with stakeholders to clarify requirements and translate data needs into technical implementation tasks.
Advantageous Requirements Hands-on exposure to Microsoft Fabric components such as Lakehouse, Warehouse, Data Factory, Notebooks, Dataflows, Semantic Models, OneLake and Power BI integration. Exposure to Azure Data Lake, Azure Data Factory, Azure Databricks, Azure SQL, Azure DevOps or similar cloud data platforms. Understanding of medallion architecture, including Bronze, Silver and Gold layers. Experience in data quality checks, reconciliation, monitoring, lineage tracking and operational support of data pipelines. Familiarity with Git, CI/CD, deployment practices and environment management. Awareness of cloud security, access control, data protection, audit requirements, Government on Commercial Cloud and IM8 requirements.