Senior Data Engineer, Risk Technology

Organization and team

Role overview

Power BI and reporting

• Design, develop, test, deploy, and support Power BI reports, dashboards, and interactive visual experiences for Investment Risk and related stakeholders.
• Build and optimize semantic models, including dimensional design, relationships, measures, calculation groups where appropriate, row- and object-level security, incremental refresh, and performance tuning.
• Write and optimize DAX and Power Query (M) using clear standards and reusable patterns.
• Partner with business users to translate analytical requirements into visual designs, prototype, validate usability, and incorporate feedback.
• Manage Fabric capacity, compute, memory, concurrency, refresh demand, workloads, and optimization; use telemetry to identify bottlenecks and recommend remediation.
• Design and support pipelines that hydrate semantic models from Snowflake, Databricks, enterprise warehouses, lakehouses, and approved sources.
• Govern Power BI access through workspaces, apps, audiences, Entra groups, role-based access, sharing, permissions, and periodic reviews.
• Build and support Power Automate flows for alerts, notifications, approvals, scheduled actions, and user-triggered workflows.
• Apply Power Automate governance, secure connection and sharing practices, monitoring, exception handling, and operational documentation.
• Promote source control, deployment pipelines, environment segregation, release controls, documentation, and CI/CD for Power BI lifecycle management.

Data engineering

• Develop complex, performant, and maintainable data engineering solutions using Azure Databricks and the listed data technologies.

Work arrangement

• The role follows a hybrid working model and requires regular in-office collaboration.

Requirements

• Strong hands-on experience developing Power BI reports, visuals, semantic models, and governed business intelligence solutions.
• Practical understanding of Fabric capacity, compute consumption, memory constraints, performance, and workload management.
• Strong data engineering capability, including reliable data pipelines and complex ETL and transformation solutions.
• Experience with Azure Databricks, Snowflake, Azure Data Factory, SQL, Python, and PySpark.
• Ability to partner with business users and technical teams to translate analytical requirements into delivered solutions.