A global leading Investment Bank is needing a Senior Spark Data Engineer to join an agile team responsible for building a strategic integration layer between Deal stores and Operations/Regulatory systems. This role is central to a multi-year program aimed at modernizing infrastructure and leveraging cloud technologies to improve scalability, resilience, and operational efficiency. This opportunity is critical to ensuring the accuracy, timeliness, and compliance of trade flows across global markets. By modernizing the underlying infrastructure, you will help reduce operational risk, improve regulatory reporting, and enable the business to scale efficiently. Design and implement a core platform that ensures accurate and timely trade execution across interconnected systems. Translate business requirements, epics, and features into reliable, scalable technical solutions. Work collaboratively within an agile delivery model, contributing to sprint planning, development, and testing.

Engage with product stakeholders to align technical delivery with regulatory and operational priorities.

You will join a technology focused group on simplifying complex processes and driving innovation. The team delivers solutions that support regulatory compliance and operational workflows, using modern data platforms and cloud-native architectures. Strong experience with Spark and PySpark for distributed data processing. Familiarity with Databricks and Delta Tables (preferred). Proven ability to design real-time and batch data pipelines using Kafka. Proficiency in CI/CD practices (GitLab, ADO, GitHub) and test-driven development. Solid understanding of cloud architecture, ideally Azure (AWS/GCP also considered). Additional experience with Java backend development, Kubernetes, and modern data infrastructure is advantageous.  To know more about this and other similar FinTech Data opportunities please apply asap – as the client is looking to move swiftly.