data engineer in banking

Barclays is a banking and financial services company that provides markets and post-trade technology, including systems for collecting, storing, processing, and analysing data.

• Build and maintain data architecture pipelines for transferring and processing durable, complete, and consistent data;
• Design and implement data warehouses and data lakes that manage required data volumes and velocity while meeting security measures;
• Develop processing and analysis algorithms for the intended data complexity and volumes;
• Collaborate with data scientists to build and deploy machine learning models;
• Advise and influence decision-making and contribute to policy development;
• Lead collaborative assignments and guide team members through structured assignments;
• Identify new directions for assignments and projects and combine cross-functional methodologies to meet required outcomes;
• Consult on complex issues and advise People Leaders on escalated issues;
• Identify ways to mitigate risk and develop policies and procedures supporting control and governance;
• Own risk management and strengthen controls related to the work;
• Collaborate with other work areas and business-aligned support functions;
• Analyse data from multiple internal and external sources to solve problems creatively and effectively;
• Communicate complex information to stakeholders;
• Influence stakeholders to achieve outcomes.

• Strong hands-on experience in ETL development, data transformation, and end-to-end pipeline workflows;
• Experience creating structured datasets and analytics outputs;
• Strong SQL, database querying, joins, and large-scale data modelling skills;
• Experience with data warehousing and dataset integration;
• Experience integrating multiple enterprise data sources, including APIs and feeds;
• Experience working with distributed data environments;
• Ability to debug pipelines, resolve data issues, and validate datasets;
• Experience with data quality frameworks and controls;
• Understanding of financial datasets;
• Ability to collaborate across teams and stakeholders;
• Ability to advise and influence decision-making;
• Ability to lead collaborative assignments and guide team members;
• Ability to analyse complex data from multiple sources and communicate complex information;
• Nice to have: Exposure to AWS, data lake architectures, catalogue-based querying environments, and machine learning model delivery.

• London office.