Data Analyst

Description 

This team is part of the Foundational Data function, where you will transform complex data and market information into clear, reliable, and actionable insights for clients worldwide. You will work with diverse data sources, support research and validation activities, and help create the strong data foundations needed for high-quality client solutions. You will join a collaborative, international team focused on learning, problem-solving and building scalable data capabilities. The level of responsibility will depend on experience, with earlier-level analysts receiving more guidance and more experienced analysts taking greater ownership of complex workstreams, methodology and process improvement. 

Core Requirements:  

• Extract, clean, organise, and analyse structured and unstructured data using Excel, SQL, and other relevant data tools. 

• Use Python or R, where applicable, for data analysis, automation, validation, and repeatable reporting workflows. 

• Work with large and complex datasets, including web-scraped, digital behavioural, website, database, and third-party source data. 

• Effectively enrich, validate, and contextualise datasets for client and internal deliverables against available and trusted sources.  

• Apply rigorous data quality, validation, and governance standards to ensure accuracy, consistency, and transparency of outputs. 

• Create clear dashboards, visualisations, reports, and presentations using appropriate analytics and business intelligence tools. 

• Interpret complex analytical findings and translate them into actionable insights for technical and non-technical audiences. 

• Support data preparation, transformation, and governance activities aligned with project methods, frameworks, and delivery standards. 

• Maintain organised project files, document assumptions and methodology, and use version control or structured documentation practices where required. 

• Collaborate with global analyst, data, research, and client-facing teams to deliver high-quality output on time. 

• Continuously build capability in data analytics, automation, visualisation, and emerging data science techniques as the role evolves.  

• Leverage AI-powered tools and automation techniques to bring efficiency in all the above workflows. 

• Working knowledge of SQL for extracting, joining, filtering, and validating data from multiple sources. 

• Working knowledge of Python or R for data analysis, automation, and reporting. 

• Strong understanding of data analysis techniques, data validation, and data quality management. 

• Experience working with large and complex datasets such as web scraped data or digital behavioural data. 

• Ability to build meaningful visualizations, dashboards, and reports using industry-standard tools such as Power BI and Tableau. 

• Strong analytical and problem-solving skills with the ability to derive actionable business insights from data. 

• Ability to communicate complex analytical findings to both technical and non-technical audiences. 

• Understanding of data structures, data pipelines, ETL processes, and data governance principles. 

• Experience using version control tools (e.g., Git) and applying good coding and documentation practices.  

• AI literacy, including effective use of AI tools, prompt engineering, automation, and agent-based workflows to enhance research, analysis, and operational efficiency.