Data Analyst – Power BI & Azure Focus

About the role

An experienced Data Analyst is needed to join a major ICT program delivering new data, intelligence, reporting and case‑management capabilities. You will work closely with Business Analysts, Data Architects, Data Engineers and business stakeholders to turn complex datasets into actionable insights that drive evidence‑based decisions.

Key responsibilities

• Engage with business stakeholders to capture operational, reporting, intelligence and analytical requirements.
• Analyse data from multiple sources to identify trends, risks, opportunities and business insights.
• Develop and maintain dashboards, reports, visualisations, metrics and performance measures using Power BI.
• Translate business requirements into data and reporting specifications for developers, engineers and architects.
• Validate data quality, accuracy, completeness and consistency across reporting products.
• Collaborate with Data Architects, Data Engineers and stakeholders to improve data quality, governance and usability.
• Support user acceptance testing, report validation and implementation activities.
• Present analytical findings and actionable insights through visualisations, reports and presentations.

Required profile

• Strong analytical and critical‑thinking abilities with a collaborative, customer‑focused approach.
• Excellent communication skills to convey complex data insights to both technical and non‑technical audiences.
• Proactive attitude toward continuous improvement and ability to manage competing priorities across multiple projects.
• Commitment to respectful, inclusive and collaborative ways of working.

Required skills

• Power BI (moderate to advanced) including Row‑Level Security.
• Advanced Microsoft Excel.
• Microsoft Fabric, Semantic Models, Dataflows and Lakehouse concepts.
• Databricks and Spark SQL for large‑scale data processing.
• DAX, Power Query and SQL.
• Data modelling and business intelligence techniques.
• Data visualisation best practices.
• Experience with data quality, governance and data management practices.