Description
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Reporting, Analytics and AI Lead
Job Summary
The Reporting, Analytics and AI Lead owns the design and delivery of analytics, data modelling, reporting, and AI-enabled consumption use cases through the enterprise data platform. This pivotal role is responsible for designing and delivering scalable, secure, and future-ready data modelling, reporting, and analytics through Azure, Fabric, and Purview, serving as the single source of truth for the enterprise. The role empowers business users to consume trusted, governed, and explainable data products through reporting, semantic models, and approved AI-enabled analytics tools. This is a technical delivery role focused on quality data, AI and analytics through the data platform—not a traditional BI role or general enterprise AI function.
Key Accountabilities
1. Lead the design and delivery of trusted data, technical specifications, and data models across Azure, Fabric, and Purview to provide integrated, secure, and scalable data for consumption
2. Own and deliver analytics, reporting, and AI-enabled consumption requirements and resulting modelling, reporting and analytics products for the enterprise data platform
3. Define, document, and validate reporting requirements, KPI definitions, business rules, management information logic, and analytics use cases. Define prioritisation, standards, delivery approach, and adoption metrics for data, AI and analytics initiatives
4. Partner with stakeholders to shape use cases, success measures, and iterative delivery plans. Deliver reporting and analytics outputs that are trusted, governed, consistent, and suitable for senior stakeholder consumption
5. Lead UAT, business validation, and sign-off for reporting outputs, semantic models, data products, and AI-generated analytical answers
6. Lead AI tools to answer business questions using approved, certified, and traceable data products. Validate AI-generated analytical answers for accuracy, context, source, assumptions, business meaning, and appropriate use
7. Deliver and integrate AI-enabled analytics and data science capabilities with governed data platforms
8. Identify opportunities to and implement outputs that reduce manual reporting and replace with governed data products, semantic models, and AI-enabled analytics consumption
9. Manage stakeholder expectations, prioritisation, adoption, feedback, and reporting rationalisation
10. Support the transition from dashboard-led reporting to governed data products, semantic models, and AI-enabled consumption through the data platform
11. Ensure AI-enabled analytics outputs are explainable, traceable, and aligned to approved metric definitions. Work with platform, governance, engineering, and modelling colleagues to ensure AI-enabled consumption is safe, controlled, and operationally supportable
12. Stay at the forefront of the latest tools, technologies, and techniques in data, reporting, AI and analytics
Core Skills
13. Expertise in semantic data modelling, including physical, dimensional, and logical models with deep understanding of BI patterns and enterprise architecture
14. Highly skilled in building consistent metrics and reporting layers across governed platforms
15. Advanced technical skills in hands-on delivery using Microsoft Azure, Fabric, and modern tools to deliver quality data through a data lakehouse
16. Strong data visualisation and UX delivery; ability to critique and uplift data and reporting quality. Strong reporting, analytics, and management information capability
17. Strong AI-enabled analytics, natural language querying, semantic consumption, and delivery of explainable analytical outputs. Ability to define and deliver safe and appropriate AI-enabled analytics use cases for the data platform
18. Ability to validate AI-generated analytical answers for accuracy, context, assumptions, source data, and business meaning
19. Working knowledge and skills in data science, AI, and machine learning applications in analytics and reporting, including responsible use controls
20. Ability to work with engineers and modellers to translate business requirements into governed data products and semantic models; proven ability to deliver KPIs, measures, dimensions, and reusable business logic
21. Skills in semantic models, certified datasets, controlled self-service analytics, and AI-enabled consumption patterns
22. Understanding of data sensitivity, access controls, governance requirements, and responsible use of AI-generated outputs. Ability to support adoption of AI-enabled analytics while managing risks, user expectations, and appropriate use
23. Leadership, coaching, stakeholder and mentoring skills
24. Passion for data and innovation to implement global best practice in modern, scalable, and future-proof reporting and analytics
Required Experience
25. Bachelor's degree in data analytics, data science, or related field
26. Extensive experience in data, AI and analytics delivery with a proven track record of delivering secure, high-value data, AI and analytics products and measurable business impact. Proven experience in semantic data model delivery.
27. Experience in leadership and line management; must have worked in a technical data team, mentored and led other team members, and worked with a Chief Data Officer
28. Extensive experience implementing Microsoft Azure and self-service data platform frameworks at scale through the consumption layer. Experience in data standardisation frameworks for harmonising data definitions, taxonomies, and formats across regions
29. Experience translating business requirements into data products, semantic models, reports, or analytical outputs
30. Experience rationalising reports, metrics, dashboards, management information processes and self-service analytics environments
31. Experience supporting senior stakeholders with trusted reporting and analytics; track record of delivering executive-certified data, AI and analytics products with high adoption. Experience validating outputs for senior management or executive reporting
32. Experience delivering data science, advanced analytics, and AI-assisted analytics capabilities integrated with governed data platforms
33. Experience working closely with data engineers, architects, and business domain leads to design and deliver technical specifications and model design
34. Experience supporting AI-enabled analytics, natural language querying, automated insights, semantic search, or AI consumption layers
35. Awareness of relevant AI governance frameworks such as NIST AI Risk Management, ISO AI Management standards, Responsible AI principles, or equivalent enterprise control frameworks
36. Relevant analytics, data, Power BI, SQL, Microsoft reporting, Azure, AI analytics, or data platform certifications