• Provide BAU support for the Enterprise Data Warehouse (EDW), including monitoring, troubleshooting, performance tuning, and issue resolution  

• Support Qlik Replicate processes and ensure reliable data ingestion across systems  

• Implement enhancements and modification requests to the EDW based on business needs  

• Perform data validation, quality checks, and root cause analysis for data issues  

• Ensure operational stability, security, and performance of live data pipelines and applications  

• Document processes, configurations, and support procedures to maintain platform resilience  

Project & Modernization Exposure  

While this is primarily a BAU-focused role, the Data Platform Engineer will also:  

• Contribute to the migration of the EDW to Microsoft Fabric  

• Gain exposure to modern cloud data technologies and evolving data architecture patterns  

• Support development work related to platform modernization initiatives  

• Work under the guidance and mentorship of senior data engineers on strategic transformation efforts.  

• Follow coding best practices and ensure that the development works comply with the best practice guidelines.  

Maintenance & continuous improvement  

• Perform ITIL Incident, Problem, and Change Management practices in accordance to SLAs and follow processes.  

• Identify key problem areas within the application and implement improvements. Evaluate and improve existing data analytics systems  

Data Expertise   

• Understand the IB’s main business processes and how it relates to data that is generated or captured.   

• Understand associated data flows and dependencies between different enterprise systems  

• Required to independently research, test and problem-solve for technical issues or blockers, obtaining guidance from Leads or Architects when required.   

• Drive the troubleshooting of key technical issues or escalate and work with appropriate teams.  

• Identify key improvement areas and discuss with the technical lead where needed.  

• Building the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources, applications and platforms.  

• Able to integrate multiple data sources & user-end applications with databases into one system. (to store the data and its retrieval from the databases)  

• Experience in designing and implementing robust data pipelines and ETL/ELT framework  

• Proven experience as a data warehouse developer, including full implementation of data warehousing solution.  

• Experience in data engineering solutions built on modern data lake or Lakehouse architectures, including Delta Lake or equivalent frameworks e.g. Microsoft Fabric. 

• Good understanding of enterprise design concepts: re-usability, continuous integration, security, scheduling, monitoring, etc  

• In-depth understanding of database management systems, online analytical processing (OLAP), SQL queries (Azure SQL DB)  

• Expertise with Azure Resource Management and templates is an added advantage  

• Exposure to cloud technologies (MS Azure, AWS) & desire to learn and deliver new things on a needs-basis. (big data, BI, data science, etc.)  

• Expertise in data warehouse design methodologies and technologies, data modelling (Data Vault modelling methodology experience is preferable), data quality and metadata