Data Engineer

About the Role

Join us as a Data Engineer at Barclays, where you will spearhead the evolution of our data platforms and real-time processing capabilities, driving innovation and operational excellence. You will harness cutting-edge cloud and streaming technologies to build and manage scalable, resilient, and secure data solutions, enabling seamless delivery of our digital and data-driven services. The role is based out of Pune.

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.

Key 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.
• Harness cutting-edge cloud and streaming technologies to build and manage scalable, resilient, and secure data solutions.
• Write clean, testable, and well-documented code following established standards.
• Create and maintain unit tests and integration tests for developed components.
• Participate in code reviews and incorporate feedback constructively.
• Troubleshoot issues using ELK stack and application logs.
• Actively participate in Scrum ceremonies and collaborate with team members.

Requirements & Tech Stack

• Experience in Databricks with creation of data products, pipelines, scheduling jobs, etc. in AWS.
• Develop Kafka Streams, Spring Kafka, and Apache Flink applications for real-time event processing.
• Implement parsers for various message formats: ISO 20022 XML, SWIFT MT, JSON, and flat files.
• Build data transformation logic to flatten hierarchical messages into target AVRO schemas.
• Implement stateful transformations including aggregations, joins, and windowed operations.
• Integrate with source systems via IBM MQ and Kafka topics.
• Strong proficiency and solid understanding of core concepts and modern features with cloud technologies such as Snowflake, AWS, Databricks.
• Hands-on experience with Spring Boot and Spring Framework.
• Strong unit testing skills using JUnit 5 and Mockito.
• Good understanding of data structures and algorithms.
• Experience with Git and collaborative development workflows.

Qualifications

• Experience with IBM MQ or JMS-based messaging.
• Knowledge of payment processing domain and creation of Data products in a Data mesh space.
• Familiarity with ELK stack for logging and troubleshooting.
• Experience with SWIFT parsing libraries (Prowide Core).
• Experience with Flink stateful processing, checkpointing, and state backends.