Data Analyst – SQL, Python, ETL, AWS Data Lake – Hybrid, Milton Keynes, UK

A global organisation are seeking an experienced Data Analyst to join their team and support data analytics initiatives within the UK retail banking environment.

The ideal candidate will be responsible for analysing complex datasets, understanding ETL processes, developing source-to-target mapping documents, gathering business requirements, and supporting data discovery and analysis across retail banking systems. Strong SQL skills, proficiency in Python or R, and hands-on experience working within AWS data lake environments are essential.

This will be a 12 month fixed term contract with an annual salary available.

This position will be worked on a hybrid basis, with 3 days per week on-site required in Milton Keynes.

Key Responsibilities:

• Analyse complex datasets and business rules to support data analytics initiatives within the UK retail banking environment.
• Gather and document data-related requirements by collaborating closely with business stakeholders and translating business needs into functional and technical specifications.
• Conduct workshops and data discovery sessions to understand data flows, dependencies, and underlying data structures.
• Develop and maintain source-to-target mapping documents to support data integration, transformation, and reporting requirements.
• Analyse and support ETL processes, ensuring data is accurately extracted, transformed, and loaded across relevant systems.
• Utilise advanced SQL skills and Python or R for data manipulation, analysis, and problem-solving.
• Work with AWS data lake environments, including S3, Athena, Redshift, and Glue, to support data analysis and investigation.
• Leverage knowledge of UK retail banking systems and data structures to interpret complex datasets and deliver meaningful insights.
• Support analysis of loyalty and rewards ecosystems, identifying data relationships, trends, and opportunities for improvement where required.
• Work closely with technical and business teams to investigate data issues, validate requirements, and ensure data quality and consistency.