Data engineer in capital markets

Join us as a Data Engineer within our Markets Post trade technology division where you will be accountable for delivering a set of enterprise, non-negotiable data strategy outcomes across the end-to-end lifecycle.

• 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.
• 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.

• Data Engineering & ETL Development: Strong hands-on experience in ETL development, data transformation, and end-to-end pipeline workflows (ingestion → processing → consumption) including structured dataset creation and analytics outputs.
• SQL, Data Modelling & Data Warehousing: Solid database and querying skills with expertise in SQL, joins, and large-scale data modelling for warehouse-style analytics and dataset integration.
• Data Integration & Distributed Systems: Experience integrating multiple enterprise data sources including API/feed handling and working with distributed data environments.
• 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.
• Будет плюсом: Cloud & Data Lake Fundamentals: Exposure to AWS, data lake architectures, and catalogue-based querying environments, supporting scalable data processing; Data Quality, Testing & Debugging: Strong capability in debugging pipelines, resolving data issues, validating datasets, and working with data quality frameworks and controls; Financial Data Domain & Collaboration: Understanding of financial datasets with ability to collaborate across teams and stakeholders to deliver data solutions

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