About This Role
This role is for a Data Specialist who will help KPMG become a data-driven and insights-led firm. You will develop, modernise, and support data solutions for reliable reporting, business process automation, operational integrations, and analytics. The position supports the ongoing modernisation of the data estate, transitioning towards a cloud-enabled, governed, and AI-ready data platform.
Responsibilities
- Design, develop and maintain data pipelines, data integrations, databases, and models supporting analytical solutions
- Build and support modern ETL/ELT patterns across cloud data platforms, including Azure, Databricks, SQL and related Azure services
- Integrate and manage data from a range of internal and external source systems, including APIs, files, cloud services, operational applications, and vendor platforms
- Develop and support curated data products that can be used by semantic models, Power BI reports, downstream integrations, and future AI-enabled consumption patterns
- Support operational data workloads, business process automations, reference/master data needs, and integration persistence using relational data platforms where appropriate
- Produce and maintain BI reporting solutions, semantic models and analytics-ready datasets that provide accurate, consistent, and timely insights
- Contribute to data governance practices including data quality, lineage, access control, metadata, sensitivity classification, and documentation
- Use Azure DevOps and modern engineering practices to manage code, deployments, releases and change control
- Collaborate with business stakeholders, technology teams, data consumers, and external vendors to understand requirements and deliver reliable, scalable solutions
- Monitor, troubleshoot and improve existing data processes to ensure performance, reliability, security, and supportability
Requirements
- Strong cloud data engineering experience, comfortable working across modern data platforms, ideally within the Microsoft Azure ecosystem
- Hands-on experience building and supporting data pipelines using technologies such as Azure Data Factory, Databricks, PySpark, Python, SQL, SSIS or similar ETL/ELT tools
- Understanding of traditional SQL Server, SSIS, data warehouse and operational data patterns, and interest in helping transition these workloads into modern cloud-based architectures
- Confident working with SQL Server and Azure SQL, including T-SQL development, stored procedures, views, indexing, relational modelling, and performance tuning
- Experience, or a strong interest, in modern platform capabilities such as Databricks, Delta Lake, Unity Catalog, Microsoft Fabric, Power BI semantic models and governed data products
- Experience integrating data from multiple sources including APIs, files, databases, cloud services, vendor platforms, and internal business systems
