Data Product Manager; Hands-On; _Product Manager II

Role Description Data Product Manager (Hands-On)

Location:

Nottingham (100% onsite)
Type:
Contractor (Inside IR35) or Fixed-Term Employment

Start Date:

ASAP

We are seeking a hands-on Data Product Manager who can bridge the gap between business strategy, data engineering, analytics, and delivery teams. This is an exciting opportunity to lead the development of high-impact data products that enable smarter business decisions, drive operational excellence, and unlock the value of enterprise data.

You’ll work closely with senior stakeholders, product teams, data engineers, analysts, and technology partners to define product roadmaps, prioritize initiatives, and ensure successful delivery of data and BI solutions across a complex, global environment.

• Define and own the vision, roadmap, and priorities for data products and BI solutions.
• Partner with business and technology stakeholders to translate strategic objectives into data-driven outcomes.
• Lead cross-functional teams across data engineering, analytics, architecture, and business functions.
• Drive delivery of data initiatives using Agile methodologies and product management best practices.
• Champion data governance, quality, security, and compliance standards.
• Prioritize product backlogs and manage competing stakeholder demands.
• Ensure data products are scalable, trusted, and aligned with enterprise data strategy.
• Present insights, recommendations, and progress updates to senior leadership and executive stakeholders.

• Proven experience in Data Product Management, Data & Analytics, Data Engineering, or a related field.
• Strong background delivering data or business intelligence projects within enterprise environments.
• Experience operating within complex, global, matrix-driven organisations.
• Demonstrated ability to manage multiple stakeholders, suppliers, and delivery teams.
• Exceptional communication and executive stakeholder management skills.
• Strong understanding of Agile delivery frameworks and product ownership principles.

• Data governance, data quality, security, and lifecycle management.
• SQL and modern cloud-based data platforms.
• Microsoft Azure, Databricks, and Power BI.
• Data modelling, ETL/ELT pipelines, and data warehousing.
• Data privacy and regulatory compliance (GDPR and related frameworks).
• Agile tools such as Jira, Confluence, Azure Dev Ops, or similar.

Business Analysis, Data Engineering, Data Modeling

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