Senior Data & Insights Analyst

About the role

The role establishes a dedicated business-facing analytics capability that turns enterprise data into clear, actionable insight. It partners with business leaders on their strategic priorities, defines trusted performance measures and aligns analytics products across Motors, Retail and Group Business data to real business needs.

Key responsibilities

• Build trusted relationships with business leaders and turn their priorities and questions into a focused analytics agenda.
• Define, standardise and socialise enterprise KPIs, measures, targets and business definitions with clear ownership.
• Shift analytics from report delivery to decisions, outcomes and value realisation.
• Align SAC, Power BI, Datasphere and modern data platform investments to priority use cases.
• Develop KPI definitions, maintain KPI catalogues and insight briefs, and explain changes, drivers and risks behind performance.
• Produce executive insight packs, management commentary and decision-support materials.
• Shape dashboards, stories and semantic models with BI specialists, and create adoption plans, training and user guidance.
• Work with Data Engineering, Data Governance and AI teams on data requirements, quality, forecasting and use cases.
• Facilitate requirements workshops, UAT and sign-off, monitor usage and feedback, and prioritise the analytics backlog.

Required profile

• Bachelor's degree in Business, Economics, Finance, Statistics, Data Analytics, Information Systems, Engineering or a related discipline.
• 5+ years in business analytics, insights, management information, business intelligence or performance management, with significant stakeholder engagement.
• Certification in business analysis, data analytics, Power BI, SAP Analytics Cloud or a related domain preferred; continued learning in data storytelling and decision science advantageous.

Required skills

• Power BI and Excel; working knowledge of SAC, Data Analyzer and Datasphere preferred.
• Working knowledge of SQL, dimensional models, semantic layers, data quality and lineage advantageous.
• Advanced trend, variance, driver, segmentation, funnel, cohort, scenario and root-cause analysis.
• Agile analytics delivery, requirements, UAT and adoption experience.