Delivery Lead/Senior Data Engineer

About

Embark on a transformative journey as a Delivery Lead/Senior Data Engineer. At Barclays, our vision is clear –to redefine the future of banking and help craft innovative solutions. In this role 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.

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.

Requirements

To be successful as a Delivery Lead/Senior Data Engineer, you should have:

• 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

Other highly valued skills include:

• Experience supporting Dev Ops and CI/CD strategies for data platforms using tools such as Jenkins and Git Lab, 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

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, digital and technology, as well as job-specific technical skills.

Location

This role is located in our Whippany, NJ office.

Salary

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

The minimum and maximum salary/rate information above include only base salary or base hourly rate. It does not include any other type of compensation or benefits that may be available.

Benefits

Barclays employees are eligible for a suite of competitive and generous employee benefits, including medical, dental and vision coverage, 401(k), life insurance, and other paid leave for qualifying circumstances.

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.
• Collaboration with data scientist to build and deploy machine learning models.