Collaborate with us
Are you a public officer looking to enhance your agency’s data engineering capabilities? Email us at dp@tech.gov.sg to explore how partnering with us can strengthen your data initiatives and drive your agency forward.
About Data Practice
What we do
Section titled “What we do”The Data Engineering Practice (DP) explores technologies, drives innovation, and standardises best practices to enable the effective and responsible use of data across the Whole of Government (WOG). At DP, we focus on three key areas to deliver great products for the Digital Government:
1. AI for Data Engineering (AIDE) for Effective Data Use
Section titled “1. AI for Data Engineering (AIDE) for Effective Data Use”AIDE leverages artificial intelligence to enhance data engineering practices, developing scalable standards and frameworks to guide agencies in effective data use and support of DGB 2.0. Our work focuses on creating robust data foundations and consistent methodologies that agencies can implement, ensuring high-quality, reliable data that enables effective AI applications and improved decision-making capabilities across government operations.
2. Privacy-Enhancing Tech (PETs) for Responsible Data Use
Section titled “2. Privacy-Enhancing Tech (PETs) for Responsible Data Use”As data sharing and AI adoption accelerate across WOG, ensuring that sensitive data can be used safely and responsibly has become more critical than ever. Our work focuses on both helping agencies better assess privacy risks and providing practical tools to mitigate them. To achieve this, we explore emerging PETs, validate them through agency pilots, and drive adoption by publishing standards/ implementations or working with other GovTech teams to operationalise into central tools.
Our portfolio spans data transformation methods (e.g., anonymisation, synthetic data generation, differential privacy, homomorphic encryption), software computation (e.g., federating computing) and operating environments (e.g., trusted execution environments).
3. Better Data Workforce and Products
Section titled “3. Better Data Workforce and Products”We will develop Golden Paths that provide agencies with standardised reference architectures, enabling them to build stronger data capabilities and deliver higher-quality data products. Through workforce transformation, we will equip public officers with practical data engineering capabilities, curated learning pathways, and reusable engineering frameworks that promote consistent and scalable practices. At the same time, we will leverage the Standard Data Platforms (SDP) to provide reference architectures that simplify how agencies build, deploy, and operate data products. By combining skilled practitioners with standardised platforms and proven practices, we aim to accelerate high-quality data product delivery and foster a sustainable, capable data workforce across WOG.
Our work and initiatives
Section titled “Our work and initiatives”Our work spans key domains such as data engineering, privacy-enhancing technologies, and data platforms, governance and quality. We deliver outputs that include advanced prototypes and agency pilots, as well as shared standards, playbooks, and training. Through co-creation with agencies, IHLs, and industry, we translate promising ideas into production-ready solutions aligned with government policy.
Specifically for data privacy, our contributions include experimental projects, research translated into practice, publications, presentations on international platforms, and papers at top-tier academic venues.
In-Depth Guides
Section titled “In-Depth Guides” Synthetic Data Generation Primer A comprehensive guide on synthetic data generation methodologies, applications, and best practices. Explore learning-based synthesis techniques, quality evaluation frameworks, privacy-preserving approaches, and real-world deployment strategies for responsible synthetic data use in the public sector.
Featured Initiatives
Section titled “Featured Initiatives” CRISPr Framework for Data Pipeline Design and Monitoring CRISPr provides a structured framework for designing and managing data pipelines, emphasising principles such as comprehensiveness, robustness, and scalability. By adopting this framework, government agencies can effectively handle data complexity and improve data quality.
CRISPr Implementation Guide This is a collection of technical guides that offer detailed, step-by-step instructions to deploy reusable data pipeline components. Each guide addresses a specific aspect of the data pipeline or technology stack, showcasing how these components can be integrated and optimised.
Data Engineering Initiative Playbook This playbook is designed to equip government agencies with foundational knowledge, practical templates, as well as useful tips and strategies to build a robust engineering framework for data-driven initiatives.
Data Engineering Tooling Suite The Data Engineering tooling suite is part of a systematic approach to the data management and transformation process, which includes data acquisition, data fusion, data access and distribution, and data exploitation.
Demystifying Differential Privacy Differential privacy is an analysis technique that helps protect personal data by adding controlled noise, allowing useful analysis without losing data quality, and making it easier to access sensitive information.
Understanding Synthetic Data Synthetic data is artificial data generated by data synthesis algorithms, which replicate patterns and the statistical properties of real data across different modalities including text, images, tabular data, audio, and video.
Get Involved
Section titled “Get Involved”Join our team
If you are passionate about public service and making lives better with tech, join our team and discover a world of development opportunities in data engineering.
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