Senior Data Engineer

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.

At Barclays, we don’t just adapt to the future – we create it. Embark on a transformative journey as a Senior Data Engineer-VP, where you will play a pivotal role in architecting and implementing high‑performance data pipelines and data flows that enable low‑latency, high‑fidelity insights. This role requires a deep interest in large‑scale data ingestion and processing across diverse formats, sources, and frequencies. You should be proficient in handling heterogeneous datasets and committed to delivering high‑quality, reliable data products. Our goal is to construct an agentic, continuously flowing data ecosystem capable of powering advanced insight engines across Research. Barclays is advancing into the agentic era by developing a robust ecosystem of talent, tooling, and modern technology to support the next generation of our data and AI capabilities. We are seeking a Senior Data Engineer for the Investment Bank’s Research Data & AI organization to help drive this transformation. Barclays Research aims to deliver differentiated, original, and data‑driven analysis for our clients. We are redesigning our Research data foundation to align with the demands of the agentic era, scaling the platform under Data Mesh principles to support distributed ownership, flexible consumption, and greater system autonomy. Success in this role requires an experienced data engineer who has worked across the full spectrum of data ingestion paradigms—including real‑time streaming and batch processing—or a strong desire to operate across all of them. This position will directly influence the strategic direction of our ingestion architecture, optimizing the full data lifecycle from acquisition and landing to transformation, entitlement, distribution, and orchestration.

To be successful in this role , you should have experience with:

• Extensive Python software engineering experience, including object‑oriented programming, package management, and scripting.

• Strong proficiency in an OOP language such as Java or C++, with solid grounding in design principles, systems engineering, and large‑scale application development.

• Deep knowledge of AWS, specifically core architecture services (S3, Route 53, EC2, EMR, EKS, Athena) and data‑processing services (AWS Glue, AWS Catalog, AWS Kinesis, Lake Formation).

• Extensive experience with distributed processing engines such as Spark, including optimization strategies, cluster‑level scaling, and operational maintenance of complex data environments.

• Practical experience orchestrating enterprise data workflows using tools such as Airflow, Dagster, Prefect or Autosys.

• Experience designing, architecting, and deploying scalable, resilient, and portable data jobs in production environments.

• Experience with large‑scale data quality frameworks such as Great Expectations or DeeQu.

• Familiarity with Data Mesh concepts—including Data Product lifecycle, Data Contracts, and Data Domain modeling.

• Experience troubleshooting, debugging, and re‑architecting data pipelines to improve efficiency, maintainability, and adherence to engineering standards.

Some of Highly Valued Skills may include:

• Domain knowledge of financial‑sector data use cases and value creation within the Research and Alpha‑generation lifecycle.

• Working knowledge of Apache Flink for real‑time distributed processing.

• Experience working with unstructured data ingestion, curation, and storage.

• Experience with data catalogs, ontology development, and taxonomy design.

• Understanding of API and system‑level communication protocols (e.g., gRPC).

• Experience with cloud‑native and containerization deployment models (e.g., Kubernetes).

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

The location of the role is Pune, IN