Description

The Data Engineer is responsible for the design, development, optimization, and support of enterprise data platforms and data integration solutions within the Risk Data and Regulatory Reporting landscape. The role focuses on enabling scalable, secure, and reliable data pipelines that support Risk, Finance, Compliance, and Regulatory reporting requirements across the organization. The role will work across complex upstream and downstream ecosystems, integrating data from source platforms into cloud-based analytical and operational data environments. This includes supporting strategic initiatives such as Global Core Banking Modernization, Project Phoenix, cloud migration, and advanced data capability development including MLOps and AI/LLM enablement. The successful candidate will collaborate closely with business analysts, architects, risk stakeholders, and engineering teams to ensure high-quality, governed, and resilient data solutions aligned to BCBS293 / RDARR principles and enterprise technology standards.

Key Responsibilities

•    Design, build, and maintain scalable data integration and transformation pipelines using Azure-based technologies. 
•    Develop and optimize data ingestion, orchestration, and transformation processes across enterprise platforms. 
•    Support migration and modernization of data platforms from on-premises to cloud-native architectures. 
•    Implement and maintain data quality, reconciliation, validation, and monitoring controls. 
•    Develop and support data solutions leveraging technologies such as Azure Data Factory, Azure Databricks, Azure Synapse, Microsoft Fabric, and Dynamics 365 integrations. 
•    Collaborate with stakeholders to translate business and regulatory requirements into technical data solutions. 
•    Ensure adherence to enterprise governance, security, and operational standards. 
•    Troubleshoot and resolve data-related incidents, performance bottlenecks, and integration issues. 
•    Contribute to CI/CD, automation, Infrastructure-as-Code, and DevOps practices within the data engineering environment. 
•    Support development of advanced analytical capabilities including MLOps and AI/LLM-enabled data services. 
•    Mentor junior engineers and contribute to engineering best practices and continuous improvement initiatives. 
•    Maintain technical documentation, lineage artefacts, and operational support documentation. 

Qualifications, Experience and Skills

•    Bachelor's degree in computer science, Information Systems, Engineering, Data Science, or a related technical discipline. 
•    Relevant Microsoft Azure certifications will be advantageous. 
•    Minimum 5–8 years' experience in data engineering, data integration, or enterprise data platform development. 
•    Proven experience delivering enterprise-scale cloud data solutions within Microsoft Azure environments. 
•    Strong experience with: Azure Data Factory (ADF), Azure Databricks, Azure Synapse Analytics, Microsoft Fabric, Dynamics 365 integrations. Azure SQL and related Azure data services 
•    Experience designing and supporting ETL/ELT pipelines and large-scale data processing solutions. 
•    Experience working with structured and semi-structured data in complex enterprise environments. 
•    Strong SQL and data modelling skills. 
•    Experience with Python, Spark, or other modern data engineering technologies. 
•    Exposure to CI/CD pipelines, DevOps practices, and Infrastructure-as-Code approaches. 
•    Experience within Risk, Finance, Banking, or Regulatory Reporting environments will be advantageous. 
•    Understanding BCBS293 / RDARR principles and data governance concepts will be beneficial. 
•    Experience supporting cloud migration and platform modernization initiatives. 
•    Exposure to MLOps, AI/LLM integration, or advanced analytics platforms would be advantageous.

Investec Culture 

At Investec we seek creative, talented people with passion, energy and stamina, who collaborate unselfishly.

We are committed to diversity and inclusion when recruiting internally and externally.