Data Governance Manager

Data Governance Manager - Enterprise Data & AI

Introduction

The IT function has renewed its strategy in response to Laing O'Rourke's ambition to help transform an industry, making it more sustainable, more productive, and fit for the future.

Our opportunity is to apply technology in ways that genuinely matter, shaping how complex projects are delivered, how decisions are made, and how innovation improves outcomes for people, communities, and the environment.

The mission is clear - to create a modern, resilient technology environment, where data underpins every decision, AI enhances every process, and digital capability accelerates progress at scale.

We are building a different kind of IT function to help achieve this, one that is trusted, forward-looking, and deeply connected to the success of the business.

You will work on meaningful technical challenges, contribute to important initiatives, and grow your capability in a supportive environment. You will be trusted with responsibility, encouraged to contribute ideas, and able to see the impact of your work.

We are looking for people who are curious, thoughtful and motivated by contributing to something larger than themselves.

Role Purpose

The Data Governance Manager - Enterprise Data & AI is responsible for implementing, embedding, and continuously improving data governance practices across Laing O'Rourke, so that data is trusted, understood, and effectively used as an enterprise asset.

Reporting to the Principal Lead - Enterprise Data & AI Enablement, this role plays a critical part in enabling the company to scale data and AI safely and effectively through clear ownership, consistent standards, and practical governance processes.

You will drive the understanding, adoption, and integration of governance practices across the company, working as part of the wider Data & AI operating model:

• Enterprise Data & AI Enablement - defines where and how data and AI are applied across the business
• Data & AI Solutions and Insight - builds and delivers the solutions that realise that value
• Data Platforms and Governance - provides trusted, secure, and scalable data foundations

• Implement and embed the enterprise data governance framework, ensuring consistent adoption across business and technology teams
• Translate governance policies into practical, usable processes and guidance
• Ensure governance is integrated into delivery life cycles, not applied retrospectively
• Drive adoption of governance practices as part of everyday ways of working

• Establish and support clear data ownership and stewardship models across business teams
• Enable data owners and stewards to understand and fulfil their responsibilities through guidance, tooling, and coaching
• Promote accountability for data as a shared enterprise asset

• Define and embed data quality management practices, including rules, monitoring, and issue resolution
• Work with the Data Platforms and Governance teams to ensure data quality controls are implemented effectively at source
• Drive improvement in data accuracy, consistency, and reliability across priority domains

• Establish and maintain enterprise capabilities for metadata management, data lineage, and data cataloguing
• Improve discoverability, understanding, and usability of data assets
• Ensure data is clearly defined, accessible, and aligned to business context

• Work closely with the Data & AI Solutions and Insight team to ensure governance supports AI and analytics use cases at scale
• Ensure data used in AI and analytics is well-governed, understood, and compliant
• Enable responsible use of data in decision-making and automated processes

• Ensure governance practices align with data privacy, security, and regulatory requirements
• Support audits, regulatory reviews, and internal assurance activities
• Promote responsible and ethical use of data across the organisation

• Act as a trusted advisor to business and technology teams on data governance, ownership, and quality
• Promote a culture of data accountability, transparency, and continuous improvement
• Ensure governance is seen as enabling better outcomes, not creating unnecessary overhead

• Define and track key metrics for data quality, ownership, and governance adoption
• Provide clear, actionable reporting on governance effectiveness and risk
• Continuously improve governance processes, tooling, and ways of working based on feedback and evolving needs