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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.
Job Responsibility
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
Requirements
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
Nice to have
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