ABOUT US

We are Data Services, our mission is to unlock the value of data by delivering high-quality, reliable, and secure data services that are accessible, understandable, and actionable. We continuously evolve our offerings, leveraging modern cloud-based technologies, and fostering strong partnerships to help our colleagues in the Bank navigate the complexities of a data-driven world and achieve their strategic objectives.

JOB DESCRIPTION

The world of data in Central Banking is evolving rapidly. With the rise of detailed data collection in financial regulation and the swift advancements in cloud-native data technologies, the demand for skilled data engineers is growing. We’re on the lookout for a mid-level Data Engineer to join our Data Engineering team. In this role, you’ll help design, build, and support data pipelines on the Bank’s strategic cloud-first data platform! Your work will play a crucial role in supporting the Bank’s core responsibilities around monetary policy, financial stability, and regulatory supervision.

• Design, develop, test, and deploy scalable, cost-effective, and secure distributed architectures using Azure services such as Azure Databricks and Azure Data Lake Storage.
• Use Databricks to ingest, transform, and load data into modern data platforms.
• Write efficient, scalable code using Python and PySpark, optimizing for performance in Azure Databricks and on-premise systems where applicable.
• Ensure data is fit-for-purpose, accurate, and available in a timely manner for business intelligence, analytics, and operational use cases.
• Monitor and optimize existing data pipelines, both cloud-native and legacy, to enhance performance, reliability, and maintainability, while minimizing impact on business operations.
• Prototype and implement modern data engineering approaches using Azure-native tools and Delta Lake, contributing to a culture of innovation and continuous improvement.
• Mentor and guide junior engineers, sharing expertise in data engineering best practices, particularly in Azure and distributed data processing using Spark.
• Work closely with business users and analysts to translate requirements into scalable data solutions using both cloud and on-prem components.
• Stay informed of emerging technologies and trends in data engineering, particularly within the Azure ecosystem and Databricks platform.
• Lead and contribute to the migration of existing data infrastructure to Azure, ensuring smooth transitions, minimal disruption, and long-term scalability.
• Collaborate cross-functionally with architects, analysts, and stakeholders to align on data strategy and ensure the interoperability of Azure-based and legacy systems.