Data & AI Capability Manager - RTG

About the Role

We are looking for an experienced Data Delivery Lead to lead the delivery of a modern data platform for a strategic enterprise client, as part of one of our early and high-priority data transformation initiatives. The project is building on an initial phase that established the target architecture, delivery approach, and proof of concept. The next phase is focused on turning that foundation into a scalable, production-ready data platform using Microsoft Azure, with Microsoft Fabric at its core. As Data Delivery Lead, you will take ownership of the data workstream from planning through delivery. You will lead the data engineering team, work closely with project and architecture leads, and ensure that the platform is delivered to a high technical and delivery standard. This role is ideal for someone who combines strong data engineering experience with the ability to lead technical delivery and work confidently with both technical and business stakeholders.

What You'll Do

• Lead the delivery of the data platform workstream, from planning throughproduction.
• Translate the existing architecture and roadmap into clear technical deliverables and priorities.
• Lead and support the team throughout the delivery.
• Design and oversee data ingestion, transformation, modelling, and data quality activities.
• Ensure the data platform is scalable, reliable, maintainable, and aligned with the agreed architecture.
• Work closely with Project Managers to manage scope, priorities, dependencies, risks, and delivery progress.
• Make and communicate technical decisions within the data engineering domain.
• Review technical solutions and deliverables to ensure quality and consistency.
• Identify technical and delivery risks early and drive them through to resolution.
• Work with other technical teams to manage integrations and dependencies across the platform.
• Help establish good data engineering practices that can be reused across future projects.
• Act as the main technical point of contact for data delivery within the project.

Requirements

Must-Have

• Proven experience leading data engineering or data platform delivery in a project environment.
• Strong hands-on experience with Microsoft Azure data services.
• Practical experience with Microsoft Fabric or a comparable modern cloud data platform.
• Strong understanding of modern data platform and lakehouse architectures.
• Experience with data ingestion, transformation, and data modelling.
• Strong SQL skills and practical experience with Python and/or PySpark.
• Experience working with data pipelines, orchestration, and data quality.
• Experience leading or mentoring data engineers.
• Ability to balance technical decisions with delivery priorities, timelines, and project constraints.
• Strong communication and stakeholder management skills.

Nice-to-Have

• Experience with dbt.
• Experience integrating data from enterprise systems (ERP).
• Experience with CI/CD and automated deployment of data workloads.
• Experience with real-time or near-real-time data processing.
• Experience with Power BI or other analytical/BI platforms.
• Experience with data governance, master data, or identity resolution.
• Relevant Microsoft Azure or data engineering certifications.

Technology

The technology stack is primarily based on Microsoft Azure and Microsoft Fabric, with

technologies and services including lakehouse architecture, OneLake, data pipelines, SQL,

PySpark, and modern data engineering practices.

You do not need to be an expert in every technology listed above. We are looking for

someone with strong fundamentals in modern data engineering who can lead the

implementation and make sound technical decisions.

What Success Looks Like

In this role, you will be successful if you can:

• Turn an established architecture and roadmap into a working production platform.
• Lead the data team effectively through delivery.
• Maintain a high standard of engineering quality.
• Anticipate and manage technical risks and dependencies.
• Deliver reliably against agreed project commitments.
• Build a data platform that provides a strong foundation for future data initiatives.