AVP : Data Engineer

Job Description

Purpose of the role

To build and maintain the systems that collect, store, process, and analyze data, such as data pipelines, data warehouses and data lakes to ensure that all data is accurate, accessible, and secure. 

Accountabilities

• Build and maintenance of data architectures pipelines that enable the transfer and processing of durable, complete and consistent data.
• Design and implementation of data warehoused and data lakes that manage the appropriate data volumes and velocity and adhere to the required security measures.
• Development of processing and analysis algorithms fit for the intended data complexity and volumes.
• Collaboration with data scientist to build and deploy machine learning models.

Assistant Vice President Expectations

• To advise and influence decision making, contribute to policy development and take responsibility for operational effectiveness. Collaborate closely with other functions/ business divisions.
• Lead a team performing complex tasks, using well developed professional knowledge and skills to deliver on work that impacts the whole business function. Set objectives and coach employees in pursuit of those objectives, appraisal of performance relative to objectives and determination of reward outcomes
• If the position has leadership responsibilities, People Leaders are expected to demonstrate a clear set of leadership behaviours to create an environment for colleagues to thrive and deliver to a consistently excellent standard. The four LEAD behaviours are: L – Listen and be authentic, E – Energise and inspire, A – Align across the enterprise, D – Develop others.
• OR for an individual contributor, they will lead collaborative assignments and guide team members through structured assignments, identify the need for the inclusion of other areas of specialisation to complete assignments. They will identify new directions for assignments and/ or projects, identifying a combination of cross functional methodologies or practices to meet required outcomes.
• Consult on complex issues; providing advice to People Leaders to support the resolution of escalated issues.
• Identify ways to mitigate risk and developing new policies/procedures in support of the control and governance agenda.
• Take ownership for managing risk and strengthening controls in relation to the work done.
• Perform work that is closely related to that of other areas, which requires understanding of how areas coordinate and contribute to the achievement of the objectives of the organisation sub-function.
• Collaborate with other areas of work, for business aligned support areas to keep up to speed with business activity and the business strategy.
• Engage in complex analysis of data from multiple sources of information, internal and external sources such as procedures and practises (in other areas, teams, companies, etc).to solve problems creatively and effectively.
• Communicate complex information. 'Complex' information could include sensitive information or information that is difficult to communicate because of its content or its audience.
• Influence or convince stakeholders to achieve outcomes.

All colleagues will be expected to demonstrate the Barclays Values of Respect, Integrity, Service, Excellence and Stewardship – our moral compass, helping us do what we believe is right. They will also be expected to demonstrate the Barclays Mindset – to Empower, Challenge and Drive – the operating manual for how we behave.

Join Barclays as a Data Engineer. T his role focuses on building scalable, secure, and high‑performance data pipelines that power enterprise‑grade Generative AI solutions. You will work across Python, PySpark, AWS, Glue, and modern data engineering tooling, enabling rapid experimentation, safe deployment, and high-quality data for AI/ML workloads. While also building reporting, insights, and analytics layers that help measure performance, adoption, and effectiveness of GenAI systems.

To be successful in this role you should have:

• Strong experience with Python (Expert) , PySpark, and AWS Glue.
• Solid understanding of distributed data processing, Spark optimisation, and scalable data design.
• Hands-on AWS data engineering experience (S3, EMR, Lambda, Step Functions, Athena).
• Proficiency in SQL for analytical modelling and reporting dataset creation.
• Experience building dashboards or following requirements from BI teams.
• Understanding of vectorisation, RAG pipelines, embeddings, and basic LLM concepts.
• Familiarity with CI/CD, Git, code quality, and automated testing for data pipelines.
• Experience with Kafka/Kinesis for streaming analytics.

Some other highly valued skills include:

• Strong communication and stakeholder management skills.
• Ability to lead technical teams and mentor junior developers.
• Collaborate with GenAI engineers, platform teams, ML engineers, data scientists, and product managers.
• Bachelor’s degree in computer science, Information Technology or Engineering is required.