AI Data Engineer
Barclays · Prague, Czechia
- On site
- Full-time
- $110,000 / year
- Prague, Czechia
Job highlights
- Engineer AI-ready data access patterns.
- Implement entitlement-aware LLM integrations.
- Design and maintain MCP integrations.
- Build data pipelines, warehouses, and lakes.
- Collaborate with data scientists and stakeholders.
About the role
AI Data Engineer at Barclays
Join us as an AI Data Engineer at Barclays, where you'll spearhead enterprise AI enablement by engineering AI-ready data access patterns, implementing entitlement-aware integrations for LLM and agentic workflows, designing and maintaining MCP integrations aligned with firm‑wide governance.
Key Responsibilities and Qualifications
To be successful as an AI Data Engineer, you should have:
- Hands-on data engineering experience with a demonstrable focus on AI and machine learning use cases, including data pipeline design and optimization for AI consumption.
- Practical experience with Model Context Protocol (MCP), including implementation in personal or enterprise projects, and understanding of context construction and integration patterns (at least as part of personal projects).
- Strong understanding of data entitlements, access controls, and governance frameworks, with ability to implement entitlement-aware systems that enforce desk-, book-, client-, and license-level constraints.
- Deep familiarity with AI/LLM concepts and terminology, including understanding of how large language models integrate with data, agentic workflows, and RAG (Retrieval-Augmented Generation) patterns.
Highly Valued Skills
Some other highly valued skills may include:
- Genuine enthusiasm and proactive drive to stay current in the rapidly evolving AI and data engineering space, with evidence of continuous learning and experimentation.
- Strong cross-functional collaboration skills with proven ability to work effectively with platform teams, AI engineers, business stakeholders, and governance teams.
- Experience with agentic AI systems and workflows, understanding how autonomous agents interact with data sources and make decisions.
Assessment Focus
You may be assessed on the key critical skills relevant for success in role, such as risk and controls, change and transformation, business acumen strategic thinking and digital and technology, as well as job-specific technical skills.
Role Purpose
To build and maintain the systems that collect, store, process, and analyse 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 scientists 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.
Key skills/competency
- AI Data Engineering
- Machine Learning
- Data Pipelines
- Model Context Protocol (MCP)
- Data Entitlements
- Access Controls
- Governance Frameworks
- LLM
- Agentic Workflows
- RAG
Skills & topics
- AI Data Engineer
- Data Engineering
- Artificial Intelligence
- Machine Learning
- Data Pipelines
- LLM
- Agentic Workflows
- RAG
- MCP
- Data Governance
How to get hired
- Tailor your resume: Highlight AI data engineering, MCP, and governance experience.
- Showcase AI/ML projects: Detail your practical experience with LLMs and RAG patterns.
- Demonstrate collaboration: Provide examples of working with diverse teams.
- Prepare for technical questions: Be ready to discuss data pipelines and access controls.
- Understand Barclays' values: Align your responses with Respect, Integrity, Service, Excellence, and Stewardship.
Technical preparation
Behavioral questions
Frequently asked questions
- What specific AI/ML use cases are prioritized for this AI Data Engineer role at Barclays?
- While the description emphasizes enterprise AI enablement, practical experience with AI/ML use cases, particularly those involving LLMs, agentic workflows, and RAG patterns, is highly valued. Candidates should be prepared to discuss their hands-on experience in designing and optimizing data pipelines for such applications.
- How important is experience with the Model Context Protocol (MCP) for the AI Data Engineer position at Barclays?
- Experience with MCP is considered practical and highly valuable. The job description specifically mentions it, indicating a strong preference for candidates who have implemented MCP in personal or enterprise projects and understand its context construction and integration patterns.
- What are the key data governance and entitlement requirements for an AI Data Engineer at Barclays?
- A strong understanding of data entitlements, access controls, and governance frameworks is critical. The role requires the ability to implement entitlement-aware systems that enforce specific constraints like desk-, book-, client-, and license-level restrictions, ensuring secure and compliant data access.
- Does the AI Data Engineer role at Barclays involve leading a team?
- The 'Assistant Vice President Expectations' section suggests that if the position has leadership responsibilities, there will be an expectation to lead a team, set objectives, coach employees, and demonstrate leadership behaviors. If it's an individual contributor role, the focus will be on leading collaborative assignments and guiding team members.
- What technical skills are essential for the AI Data Engineer role at Barclays beyond AI and data engineering fundamentals?
- Beyond core AI data engineering, essential technical skills include practical experience with MCP, a deep understanding of AI/LLM concepts (including RAG), and the ability to implement entitlement-aware data systems. Familiarity with building and maintaining data pipelines, data warehouses, and data lakes is also crucial.
- How does Barclays assess candidates for the AI Data Engineer role regarding soft skills?
- Candidates will be assessed on critical skills such as risk and controls, change and transformation, business acumen, strategic thinking, and digital and technology. Strong cross-functional collaboration skills are also highly valued, with an emphasis on working effectively with platform teams, AI engineers, business stakeholders, and governance teams.