Job Summary
The Data & Reporting Analyst is responsible for designing, developing, and maintaining high quality data models, datasets, and business intelligence solutions that enable effective decision making across the organisation.\r\n\r\nThe role focuses on turning complex data into trusted, well governed, and accessible insights through robust data pipelines, semantic models, and intuitive dashboards. Working closely with stakeholders, analysts, and data platform teams, the role ensures reporting solutions are scalable, reliable, and aligned to business outcomes.\r\nThis role is critical to improving data maturity, consistency, and confidence in performance reporting.\r\n\r\nThis role embraces the responsible use of AI to enhance productivity, insight discovery, and quality in business intelligence solutions. AI tools are used to accelerate analysis and development, while accountability, judgement, and governance remain with the developer.
Key Responsibilities
\r\nBusiness Intelligence Development\r\n Design, build, and maintain BI dashboards, reports, and scorecards using tools such as Power BI (or equivalent).\r\n Develop reusable and scalable semantic models and datasets to support self service reporting.\r\n Translate business requirements into clear technical designs and reporting solutions.\r\n Ensure dashboards are intuitive, accessible, and focused on decision making rather than raw data.\r\nData Engineering & Modelling (BI focused)\r\n Transform, cleanse, and model data from multiple source systems using SQL, Power Query, and/or scripting languages.\r\n Design star schema and dimensional models optimised for analytics and performance.\r\n Implement data validation checks to improve data quality and trust in reporting outputs.\r\n Support index scores, KPIs, and composite measures using consistent calculation logic.\r\nStakeholder Engagement\r\n Work closely with business stakeholders to understand reporting needs, measures, and success criteria.\r\n Provide guidance on metric definitions, targets, and appropriate use of data.\r\n Support analysts and business users in using BI tools and interpreting insights.\r\nData Governance & Standards\r\n Contribute to data governance activities including data quality assessments, metric definitions, and lineage documentation.\r\n Maintain data dictionaries and KPI catalogues to ensure consistency and transparency.\r\n Ensure BI solutions comply with data protection, security, and internal governance standards\r\nContinuous Improvement\r\n Identify opportunities to automate manual reporting and improve efficiency.\r\n Promote best practice in BI development, performance optimisation, and deployment.\r\n Stay current with BI tooling, visualisation practices, and analytics trends.\r\nAI Assisted Development & Productivity\r\n Use AI tools (e.g. Microsoft Copilot, Power BI Copilot, AI assisted coding tools) to accelerate BI development activities such as: \r\no Drafting and refining Power Query, SQL, and DAX logic\r\no Exploring data patterns and anomalies\r\no Generating first pass summaries and narrative insights\r\n Apply professional judgement to validate, refine, and take full ownership of all AI assisted outputs before use in production reporting.\r\n Leverage AI to reduce manual, repetitive development steps while maintaining accuracy, transparency, and auditability. \r\nAI Augmented Insight Generation\r\n Use AI supported analysis to: \r\no Identify trends, clusters, and drivers in large or complex datasets\r\no Support root cause analysis, performance diagnostics, and insight discovery\r\n Translate AI generated findings into clear, explainable insights suitable for operational and executive audiences.\r\n Ensure all AI assisted insights are backed by data, clear logic, and reproducible calculations. \r\nResponsible Use of AI\r\n Apply Responsible AI principles in all BI and analytics work, ensuring: \r\no No uncontrolled automation of decision making\r\no Transparency of assumptions and calculations\r\no Appropriate handling of sensitive or regulated data\r\n Ensure AI tools are used only within approved enterprise environments and data boundaries.\r\n Clearly distinguish AI assisted outputs from authoritative metrics and governed KPIs
Skill Requirements
Strong experience developing BI solutions using Power BI (or similar enterprise BI tools).\r\n Solid SQL skills for querying and transforming data.\r\n Experience with data modelling (star schema, facts and dimensions).\r\n Strong understanding of KPIs, targets, percentages, and composite/index measures.\r\n Experience using Power Query / ETL tools for data preparation.\r\n Scripting experience (e.g. Python) for advanced data transformation or automation.\r\n Understanding of data governance, metadata management, or metric catalogues. \r\n Practical experience using AI tools as development accelerators, not decision makers. \r\n Ability to critically assess AI generated outputs and identify errors, bias, or incorrect assumptions. \r\n Strong understanding of data fundamentals (quality, lineage, definitions) to prevent misuse of AI generated content.\r\n Experience using Power BI Copilot, Copilot in Excel, or prompt driven analytics tools. \r\n\r\nAnalytical & Communication\r\n Ability to translate complex data into clear, actionable insights.\r\n Experience engaging with non technical stakeholders and capturing reporting requirements.\r\n Strong attention to detail and commitment to data quality and consistency.\r\n Familiarity with using AI to assist with exploratory analysis, insight summarisation, or documentation. \r\n\r\nWays of Working\r\n Experience working in a governed enterprise data environment.\r\n Comfortable working across multiple priorities and stakeholder groups.\r\n Collaborative, proactive, and improvement focused mindset.\r\n Experience supporting executive level or operational performance reporting.\r\n Awareness of organizational Responsible AI standards and data ethics practices
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