Data Engineer - Integrated E-Services [ITE Headquarters] – 2 Yr Cr

[What the role is]

[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.