Job Description

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.

Vice President Expectations

• To 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. They may also 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..
• If the position has leadership responsibilities, People Leaders are expected to demonstrate a clear set of leadership behaviours to create an environment for colleagues to thrive and deliver to a consistently excellent standard. The four LEAD behaviours are: L – Listen and be authentic, E – Energise and inspire, A – Align across the enterprise, D – Develop others..
• OR for an individual contributor, they will be a subject matter expert within own discipline and will guide technical direction. They will lead collaborative, multi-year assignments and guide team members through structured assignments, identify the need for the inclusion of other areas of specialisation to complete assignments. They will train, guide and coach less experienced specialists and provide information affecting long term profits, organisational risks and strategic decisions..
• Advise key stakeholders, including functional leadership teams and senior management on functional and cross functional areas of impact and alignment.
• Manage and mitigate risks through assessment, in support of the control and governance agenda.
• Demonstrate leadership and accountability for managing risk and strengthening controls in relation to the work your team does.
• Demonstrate comprehensive understanding of the organisation functions to contribute to achieving the goals of the business.
• Collaborate with other areas of work, for business aligned support areas to keep up to speed with business activity and the business strategies.
• 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.

All colleagues will be expected to demonstrate the Barclays Values of Respect, Integrity, Service, Excellence and Stewardship – our moral compass, helping us do what we believe is right. They will also be expected to demonstrate the Barclays Mindset – to Empower, Challenge and Drive – the operating manual for how we behave.

Join us as a Delivery Lead/Senior Data Engineer 3. At Barclays, we don’t just adapt to the future, we create it. As a Delivery Lead/Senior Data Engineer 3 you will 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.

To be a successful Engineering Manager, you should have experience with:

• Proven leadership experience delivering enterprise‑scale data platforms and architectures in complex, regulated environments. Deep expertise in cloud data architecture and distributed computing paradigms, with strong hands‑on background in AWS data platforms, including Glue, Lambda, S3, Redshift, Athena, and Databricks

• Strong experience with modern analytical data platforms such as Snowflake, including architecture design, data modelling, workload optimization, and cost governance. 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. Strong understanding of cloud security, IAM, data access controls, and platform governance, with experience implementing fine‑grained data security using tools such as Immuta

• Advanced proficiency in Python, PySpark, and SQL, with the ability to guide teams on performance optimization and scalable design rather than individual contribution alone. 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.

• Strong knowledge of data governance, metadata management, data quality, and data mesh concepts, with the ability to influence enterprise‑wide adoption. Ability to communicate complex technical concepts clearly to senior leadership, influencing architectural and investment decisions.

Additional relevant skills given below are highly valued:

• Leadership exposure to real‑time and event‑driven architectures, including Apache Kafka, Spark Streaming, or similar technologies. Strategic understanding of DBT (Data Build Tool) and analytics engineering practices for scalable transformation and modelling.

• Experience operating data platforms within regulatory, risk‑controlled, or large financial services environments Experience supporting or enabling machine learning and AI workloads (including model training, inference, or feature pipelines) in partnership with Data Science or AI teams.

You may be assessed on 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 is based in Pune.