Interim Data Delivery Manager

Initial 3-6-month contract - Interim Data Delivery Manager - Incredible opportunity with a global market leading brand.

Role Summary

• Lead one or more data-focussed delivery teams to ensure successful delivery of high-quality data products and services in a way that aligns to the goals of the value streams and our data strategy.
• Negotiate and agree with value stream stakeholders on delivery objectives, scope, deliverables, timescales, and budgets.
• Communicate and collaborate with value stream stakeholders to provide visibility into delivery progress, capacity, and risks
• Drive effective execution of prioritised backlogs, aligning with business goals and technical requirements.
• Be accountable for the end-to-end delivery of projects including anticipating, identifying and mitigating project risks and managing cross team dependencies.

Responsibilities

• Overseeing delivery of a range of different data projects across a diverse variety of business functions
• Manage changes in delivery scope or requirements, negotiating as necessary with business stakeholders.
• Consider and manage non-functional requirements such as information security, data protection (GDPR), and compliance with internal standards.
• Proactively map and manage interdependencies, identifying and addressing delivery risks.
• Collaborate with product, service, and technical owners to refine and prioritise backlogs, ensuring alignment with business outcomes.
• Monitor tasks, milestones, and resources weekly against the delivery plan. Provide regular reports and updates in line with programme governance and business stakeholder requirements.

Technical Capabilities

• Understand the high-level solution architecture of the data service or platform and its interface points.
• Collaborate with data architects to align delivery outputs with enterprise architecture principles.
• Evaluate emerging technologies and methodologies to enhance delivery practices.
• Have an awareness of different technologies used within a typical data stack, such as Databricks, Azure Data Factory and Power BI.
• Knowledge of data warehousing and data modelling approaches and appropriate use.