Delivery Lead/Senior Data Engineer

About the role

At Barclays, our vision is to redefine the future of banking and help craft innovative solutions. As a Delivery Lead/Senior Data Engineer, 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 directly with AWS technologies to design and engineer scalable, secure solutions, contributing directly to the modernization of Barclays' data and technology landscape.

Responsibilities

• Build and maintain data architectures and pipelines that enable the transfer and processing of durable, complete, and consistent data.
• Design and implement data warehouses and data lakes that manage the appropriate data volumes and velocities and adhere to the required security measures.
• Develop processing and analysis algorithms fit for the intended data complexity and volumes.
• Collaborate with data scientists to build and deploy machine learning models.

Requirements

• Validated experience supporting enterprise data architecture strategies, defining standards and reference architectures, and balancing performance, scalability, and resilience.
• Highly skilled in cloud data architecture and distributed computing paradigms, with extensive applied experience leveraging AWS data platforms such as Glue, Lambda, S3, Redshift, Athena, and Databricks.
• Advanced knowledge of data modeling techniques, including dimensional modeling, schema evolution, and design patterns for analytics, reporting, and downstream data consumption.
• Demonstrated ability to define, implement, 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 serving solely as a team member contributor.

Qualifications

• Experience supporting DevOps and CI/CD strategies for data platforms using tools such as Jenkins and GitLab, embedding quality, automation, and reliability into delivery pipelines.
• Ample knowledge of data governance, metadata management, data quality, and data mesh concepts, with the ability to influence enterprise-wide adoption.
• Experience supporting or enabling machine learning and AI workloads, including model training, inference, or feature pipelines in partnership with Data Science or AI teams.
• Considerable 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.

Skills

• Experience supporting enterprise data architecture strategies, defining standards and reference architectures, and balancing performance, scalability, and resilience.
• Highly skilled in cloud data architecture and distributed computing paradigms, with extensive applied experience leveraging AWS data platforms such as Glue, Lambda, S3, Redshift, Athena, and Databricks.
• Advanced knowledge of data modeling techniques, including dimensional modeling, schema evolution, and design patterns for analytics, reporting, and downstream data consumption.
• Demonstrated ability to define, implement, 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 serving solely as a team member contributor.
• Experience supporting DevOps and CI/CD strategies for data platforms using tools such as Jenkins and GitLab, embedding quality, automation, and reliability into delivery pipelines.
• Ample knowledge of data governance, metadata management, data quality, and data mesh concepts, with the ability to influence enterprise-wide adoption.
• Experience supporting or enabling machine learning and AI workloads, including model training, inference, or feature pipelines in partnership with Data Science or AI teams.
• Considerable 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.

Benefits

This role offers a unique opportunity to deepen your cloud knowledge, work on large-scale enterprise platforms, and identify your engineering impact in real-world banking—advancing data architecture, modeling standards, and platform excellence that enable quality analytics, responsible AI, regulatory compliance, and informed decision-making at scale.

Pay

Minimum Salary: $170,000
Maximum Salary: $230,000

Schedule

The role is located in our Whippany, NJ office.