data engineer in banking

Barclays is a banking and financial services organization that transforms complex data into strategic assets supporting innovation, regulatory compliance, advanced analytics, and AI.

• Build and maintain data architecture pipelines for transferring and processing durable, complete, and consistent data;
• Design and implement data warehouses and data lakes that handle 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;
• Contribute to or set strategy, drive requirements, and recommend changes;
• Plan resources, budgets, and policies;
• Manage and maintain policies and processes;
• Deliver continuous improvements and escalate policy or procedure breaches;
• Guide technical direction as a subject matter expert within the discipline;
• Lead collaborative, multi-year assignments;
• Guide team members through structured assignments;
• Identify when additional areas of specialization are needed;
• Train, guide, and coach less experienced specialists;
• Provide information affecting long-term profits, organizational risks, and strategic decisions;
• Advise functional leadership teams and senior management on functional and cross-functional impacts and alignment;
• Manage and mitigate risks through assessment in support of the control and governance agenda;
• Demonstrate leadership and accountability for managing risk and strengthening controls;
• Collaborate with other work areas to stay aligned with business activity and strategies;
• Create solutions by comparing and selecting complex alternatives;
• Apply extensive research to problem-solving processes;
• Build and maintain trusting relationships and partnerships with internal and external stakeholders.

• Experience with cloud data warehousing solutions such as Snowflake, Amazon Redshift, or AWS;
• Understanding of data integration processes and tools such as Databricks, DBT, or Glue;
• Ability to design data models aligned with ETL pipelines;
• Understanding of data management standards, regulatory compliance including GDPR and HIPAA, and security protocols;
• Deep knowledge of entity-relationship modeling, normalization and denormalization, and dimensional modeling;
• Proficiency with data modeling tools such as erwin Data Modeler, SAP PowerDesigner, or ER/Studio;
• Advanced SQL skills, including DDL and DML;
• Ability to profile, analyze, and manipulate data in relational database management systems;
• Ability to translate complex business requirements into structured, efficient, and scalable data models;
• Ability to ensure data integrity and optimize database performance through indexing and partitioning;
• Excellent verbal and written communication skills for explaining technical data concepts to non-technical stakeholders;
• Experience working in agile teams with data engineers, architects, and business analysts;
• Ability to demonstrate risk and controls, change and transformation, business acumen, strategic thinking, and digital and technology skills;
• Nice to have: Experience guiding team members, training and coaching less experienced specialists, and advising senior stakeholders.

• Office role in Glasgow.