The Opportunity
We are supporting a major data platform transformation within a banking environment, moving from a legacy SQL Server and SSIS-based setup to a modern, scalable architecture built on dbt, Dagster, and OpenShift.
This role is not about maintaining existing systems. It is about rebuilding a critical data platform from the ground up, with direct impact on risk, trading PnL, and core financial data flows.
We are looking for a hands-on Senior Data Engineer who can take ownership of complex migration workstreams and deliver reliably in a regulated, high-stakes environment.
What You Will Do
You will play a central role in the end-to-end migration and modernisation of the data platform.
Platform Transformation
- Translate legacy ETL logic from SSIS and stored procedures into modern ELT pipelines using dbt
- Implement Data Vault 2.0 structures including Raw Vault and Business Vault
- Build datamarts and curated datasets for downstream analytics and reporting
Orchestration & Infrastructure
- Design and operate workflows using Dagster, including scheduling, dependencies, and recovery mechanisms
- Deploy and run data workloads on OpenShift / Kubernetes environments
Event-Driven Data Processing
- Enable near real-time data processing using Kafka-triggered pipelines
- Integrate with upstream data lake environments and external data providers
Data Quality & Validation
- Establish robust data validation and reconciliation processes
- Implement automated testing and monitoring using dbt
Operational Ownership
- Support production pipelines and resolve incidents when required
- Create clear documentation and ensure operational readiness
- Continuously improve performance, reliability, and maintainability
What You Will Do
You will play a central role in the end-to-end migration and modernisation of the data platform.
Platform Transformation
- Translate legacy ETL logic from SSIS and stored procedures into modern ELT pipelines using dbt
- Implement Data Vault 2.0 structures including Raw Vault and Business Vault
- Build datamarts and curated datasets for downstream analytics and reporting
Orchestration & Infrastructure
- Design and operate workflows using Dagster, including scheduling, dependencies, and recovery mechanisms
- Deploy and run data workloads on OpenShift / Kubernetes environments
Event-Driven Data Processing
- Enable near real-time data processing using Kafka-triggered pipelines
- Integrate with upstream data lake environments and external data providers
Data Quality & Validation
- Establish robust data validation and reconciliation processes
- Implement automated testing and monitoring using dbt
Operational Ownership
- Support production pipelines and resolve incidents when required
- Create clear documentation and ensure operational readiness
- Continuously improve performance, reliability, and maintainability