Data Analyst/Analytics

Key Responsibilities

• Own the design, development, and oversight of data transformation pipelines.
• Analyse large datasets to identify and resolve data quality issues, working with Finance and Portfolio Monitoring to trace discrepancies back to source and implement sustainable fixes.
• Ensure data quality, accuracy, and consistency across multiple platforms and vendors.
• Build and maintain visualisations, charts, and interactive dashboards.
• Translate complex data into meaningful business insights for senior stakeholders.
• Drive process improvements, automation, and operational efficiencies across Finance and other business functions.
• Apply AI technologies to solve real business problems and identify opportunities to enhance reporting, analytics, and decision-making.
• Work closely with technology vendors and internal stakeholders to support data connectivity and platform integrations.
• Identify practical opportunities to embed AI within the firm's data and reporting environment.
• Build and maintain visualisations, charts and interactive dashboards

Skills Required

• 4 to 5 years' experience in an analytics or intelligence function within asset management or broader financial services, with a university degree in an analytical-related subject (minimum 2:1).
• Experience in Databricks, Python, and SQL, including building and owning end-to-end data transformation pipelines.
• Experience building and maintaining visualisations and charts for business consumption. (Power BI, Alteryx, or Qlik or similar)
• Demonstrated commercial experience in leveraging AI technologies to solve business challenges.
• Experience working with and managing technology vendors or outsource providers to ensure reliable data connectivity and delivery.
• Strong stakeholder management and excellent communication skills.
• Demonstrated initiative and takes responsibility for delivering outcomes.
• Excellent communication and stakeholder skills and a proactive mindset.
• Experience supporting a technology build-out or platform implementations (eg: eFront or Appian, or similar platforms).
• Experience working with and managing technology vendors or outsource providers
• Familiarity with Azure or other cloud-based data architectures.
• Knowledge of modern fund operations and investment data platforms is desirable.