Description:
This is a pivotal Senior Data Engineer role at the heart of SSE’s assurance transformation, owning the Assurance Data Platform and enabling data-led audit, control monitoring and executive-level insight. The role offers the opportunity to shape scalable data foundations for analytics, automation and AI, while working closely with Risk, Audit, Technology and business stakeholders across the Group.
You will
- Take ownership of a data platform that helps teams use reliable information to support audit, risk and assurance work.
- Build and improve data pipelines, models and reports that make complex information easier to access, understand and use.
- Work with colleagues across technology, risk, audit and business teams to understand data needs and turn them into practical solutions.
- Put strong data quality, security and governance checks in place so that information is accurate, trusted and well controlled.
- Help shape the future of data-led assurance by supporting automation, continuous monitoring, advanced analytics and AI-ready data foundations.
You have
- Strong hands-on experience with modern data engineering tools, including Azure Data Factory, SQL, Databricks and Power BI; experience with AWS would be an advantage.
- Strong experience designing relational data models, including star and snowflake schemas, with the ability to choose the right modelling approach for different analytical, reporting and operational use cases.
- Experience building, orchestrating and automating complex data pipelines and workflows, including scheduling, monitoring, error handling and recovery.
- Good understanding of data governance, data retention, access control and data quality practices, with the ability to build these into technical solutions from the outset.
- Experience working with CI/CD practices for data engineering, including version control, automated deployment, testing and release management.
- Experience working with audit, risk, controls or assurance teams would be an advantage, including an understanding of how data can support control testing, risk monitoring and evidence-based assurance.