Data Analyst

The Senior Data Analyst supports the delivery of custom research studies that use multiple data sources and analytical methods. The role helps transform large, varied and sometimes complex datasets into accurate, well-documented and client-ready outputs that enable reliable insight and value for clients.

Working as an individual contributor under guidance from experienced colleagues, the role contributes across data sourcing, enrichment, transformation, modelling, validation, documentation and final-output preparation. It offers structured exposure to foundational data delivery, quality assurance and cross-functional collaboration.

Role context

Foundational Data includes the sources and assets that underpin data solutions and analytics, such as public-domain data, proprietary data, online and digital data, clickstream, trade data, company data and other market information.

Within the Data Foundations function, the Senior Data Analyst works with data, consulting, commercial, product and delivery colleagues to turn complex datasets into dependable outputs. The role supports market intelligence solutions, including industry benchmark studies and large-data assignments, while developing strong delivery judgement and technical capability.

Duties and accountabilities

·    Support foundational data delivery: Contribute to custom data studies from source assessment and data preparation through enrichment, modelling, validation, documentation and final-output readiness.

·    Project Management: Lead end-to-end data delivery for data consulting and custom research projects, from data sourcing and enrichment through modelling, validation, and final client delivery. 

·    Apply data methodologies: Learn and consistently apply approved methods and estimation frameworks used to transform foundational datasets into robust market estimates and actionable insights.

·    Perform data quality checks: Complete defined validation routines, reconcile anomalies, document findings and escalate material quality issues promptly.

·    Prepare and transform data: Clean, structure, combine and enrich data from multiple sources using suitable analytical tools and repeatable processes.

·    Maintain clear documentation: Record sources, assumptions, transformation logic, validation evidence, limitations and handover information to support traceability and reuse.

·    Translate data into usable outputs: Present findings clearly and accurately for technical and non-technical audiences, with support from senior colleagues where needed.

·    Support project governance: Follow established delivery toolkits, checklists, information-handling requirements and quality-governance standards throughout the project lifecycle.