Data Engineering

Spearheading continuous innovation in data engineering across GovTech and the public sector

What we do

The Data Engineering Practice (DP) serves as a main conduit for the transference of data engineering expertise to the public sector, enabling agencies to expand the quality, availability, and accessibility of their data. At DP, we focus on three key areas to deliver great products for the Digital Government:

1. Exploring technologies to conduct proof of concepts (POC)

This involves evaluating emerging tools and methodologies to enhance our data engineering capabilities. By investigating innovative solutions, we can identify technologies that address specific business challenges and yield valuable insights for future initiatives.

2. Driving innovation to empower agencies through SME-led prototypes and pilots

This focuses on providing Subject Matter Expert (SME) consulting to agencies for developing and testing scalable prototypes and conducting pilots. The SME-driven approach demonstrates the feasibility and impact of innovative solutions, engages stakeholders in real-world scenarios, gathers essential feedback, mitigates risks, and fosters a culture of creativity, ultimately delivering meaningful value through specialised guidance.

3. Standardising best practices to drive efficiency and consistency

This strives to create a more unified and efficient data engineering ecosystem by establishing clear guidelines, templates and documentation for various aspects of data engineering. By doing so, we can promote collaboration, reduce errors, and improve overall data quality while fostering a culture of innovation within a structured framework.

Our work and 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.

[TechPass Required] CRISPr Framework for Data Engineering

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.

[TechPass Required] Data Engineering Initiative Playbook

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. Learn more about differential privacy through this article.

Sharing Data with Differential Privacy

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. Learn more about the applications and challenges of synthetic data.

Synthetic Data: Navigating Its Methodologies, Applications and Challenges

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. Learn more about the tooling suite and how to master data pipelines.

Empowering Public Officers: A Data Engineering Masterclass for the Public Service

Who should join us

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.

Join our team

Explore career opportunities at GovTech.