Senior Data Lead

Embark on a transformative journey as a Senior Data Lead at Barclays, where you’ll play a critical role in building cloud‑native, data‑driven platforms that power advanced analytics, AI, and smarter banking outcomes. You’ll work hands‑on with AWS technologies to design and engineer scalable, secure solutions, contributing directly to the modernization of Barclays’ data and technology landscape. This role offers a unique opportunity to deepen your cloud expertise, work on large‑scale enterprise platforms, and see your engineering impact reflected in real‑world banking—supporting data architecture strategy, data modelling standards, platform evolution, and engineering excellence that enable high‑quality analytics, responsible AI adoption, regulatory compliance, and informed decision‑making at scale.

To be successful as a Senior Data Lead, you should have: Experience in leading enterprise data architecture decisions, defining standards and reference architectures, and balancing performance, resilience. Deep expertise in cloud data architecture and distributed computing paradigms , with extensive hands‑on background in AWS data platforms , including Glue, Lambda, S3, Redshift, Athena , and Databricks .

Advanced knowledge of data modelling techniques , including dimensional modelling, schema evolution, and design patterns for analytics, reporting, and downstream consumption. Demonstrated ability to define and govern data architecture standards , reference architectures, and engineering frameworks across multiple teams. Advanced proficiency in Python, PySpark, and SQL , with the ability to guide teams on performance optimization and scalable design rather than individual contribution alone.

Other highly valued skills may include: Experience leading DevOps and CI/CD strategies for data platforms using tools such as Jenkins and GitLab , embedding quality, automation, and reliability into delivery pipelines. Considerable knowledge of reporting and analytics toolset (Power BI, Tableau) Experience supporting or enabling machine learning and AI workloads (including model training, inference, or feature pipelines) in partnership with Data Science or AI teams.

Good understanding of cloud security, IAM, data access controls, and platform governance , with experience implementing fine‑grained data security using tools such as Immuta . Strategic understanding of DBT (Data Build Tool) and analytics engineering practices for scalable transformation and modelling. 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.

This role will be based in our Prague office. Entry Salary: Kč 1,830,000.00 Upper Salary: Kč 2,460,000.00 The entry and upper salary information above includes only annual full-time equivalent base salary and represents the typical range of pay for the role. The actual pay rate will reflect the responsibility level of the role and experience level of the individual. The entry and upper salary information does not include any other type of compensation or benefits that may be available.

Barclays employees are also eligible for a suite of competitive country-specific benefits. This position is eligible for an incentive award. Purpose of the role 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. Collaborati