Delivery Lead/Senior Data Engineer

Barclays•Jefferson, CO

1d•Onsite

About The Position

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

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
Risk and controls
Change and transformation
Business acumen
Strategic thinking
Digital and technology
Job-specific technical skills

Nice To Haves

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

Responsibilities

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.
Contribute or set strategy, drive requirements and make recommendations for change.
Plan resources, budgets, and policies; manage and maintain policies/ processes; deliver continuous improvements and escalate breaches of policies/procedures.
If managing a team, they define jobs and responsibilities, planning for the department’s future needs and operations, counselling employees on performance and contributing to employee pay decisions/changes.
Lead a number of specialists to influence the operations of a department, in alignment with strategic as well as tactical priorities, while balancing short and long term goals and ensuring that budgets and schedules meet corporate requirements.
Demonstrate leadership and accountability for managing risk and strengthening controls in relation to the work your team does.
Create solutions based on sophisticated analytical thought comparing and selecting complex alternatives.
In-depth analysis with interpretative thinking will be required to define problems and develop innovative solutions.
Adopt and include the outcomes of extensive research in problem solving processes.
Seek out, build and maintain trusting relationships and partnerships with internal and external stakeholders in order to accomplish key business objectives, using influencing and negotiating skills to achieve outcomes.