interactive investor

Data Analyst

• Extract and analyse data from our data lakes and relevant sources to provide actionable insights for business decisions and strategy formulation
• Develop, maintain, and automate insightful BI and MI reports, ensuring data accuracy and relevance
• Work to automate reporting capability through effective use of SQL, Python, Power BI, and other tools to streamline the data analysis process
• Develop strong partnerships with stakeholders from Product, Commercial, Technology, Customer Services, and Operations to support their data needs
• Lead or contribute to the development and tracking of KPIs and data insights across the company
• Utilise platforms such as SQL, Snowflake, Power BI for in-depth analysis and to create insightful reports
• Apply data science methodologies to analyse customer behaviour patterns and assess business impacts
• Leverage existing AI and machine learning platforms to enhance analytical capabilities
• Conduct comprehensive data analysis across all channels and systems, collaborating with subject matter experts
• Mentor and support team members in best practices and analytical techniques
• Ensure adherence to data protection laws and company policies, maintaining privacy and security standards
• Actively engage with various departments to integrate data-driven insights into business processes
• Commit to ongoing professional development in data analytics
• Regularly track and report on key performance indicators relevant to the role's domain
• Proactively seek and implement innovative solutions to enhance analytics capabilities

• Analyse Data and Generate Insights: Extract and analyse data from our data lakes and relevant sources to provide actionable insights for business decisions and strategy formulation
• Reporting and Visualisation: Develop, maintain, and automate insightful BI and MI reports, ensuring data accuracy and relevance. Champion the automation of reporting capabilities to enhance efficiency
• Reporting Automation: Work to automate reporting capability through effective use of SQL, Python, Power BI, and other tools to streamline the data analysis process
• Collaborative Analysis: Develop strong partnerships with stakeholders from Product, Commercial, Technology, Customer Services, and Operations, etc., to understand requirements, to support their data needs and encourage the leveraging of data for product and service improvements, and create a data-led culture
• KPI and Data Insight Development: Lead or contribute to the development and tracking of KPIs and data insights across the company, ensuring a consistent set of measures is used for decision-making.
• Analytics Platforms: Utilise platforms such as SQL, Snowflake, Power BI, Usabilla, Google Analytics, Optimizely, ContentSquare, and Hotjar for in-depth analysis and to create insightful reports
• Data Science Techniques: Apply data science methodologies, for example, statistical modeling, segmentation analysis, time series analysis, and some predictive techniques to analyse customer behaviour patterns and assess business impacts, collaborating with Data and Innovation team members across different roles to deliver actionable insights
• AI/ML Power User: Leverage existing AI and machine learning platforms as a power user to enhance analytical capabilities and productivity
• Cross-Channel Analysis: Conduct comprehensive data analysis across all channels and systems, collaborating with subject matter experts
• Mentorship and Team Support: Mentor and support team members in best practices and analytical techniques
• Data Governance and Compliance: Ensure adherence to data protection laws and company policies, maintaining privacy and security standards across all data handling activities
• Cross-functional Collaboration: Actively engage with various departments to integrate data-driven insights into business processes, enhancing decision-making and strategic initiatives
• Continuous Learning: Commit to ongoing professional development in data analytics, staying current with industry trends, tools, and best practices
• Performance Monitoring & Reporting: Regularly track and report on key performance indicators relevant to each role's domain, contributing to the overall success metrics of the team
• Innovation and Continuous Improvement: Proactively seek and implement innovative solutions to enhance analytics capabilities and drive continuous improvement in processes and outcomes
• Team Collaboration and Problem-Solving: Participate in team problem-solving sessions, share knowledge, and collaborate on projects to achieve common goals and resolve issues efficiently