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
What You Bring
Technical Expertise
Strong experience with SQL Server and T-SQL, including performance optimisation
Proven hands-on experience with dbt in production environments
Solid experience with workflow orchestration tools, ideally Dagster
Practical knowledge of Data Vault 2.0 modelling concepts
Experience working with container platforms such as OpenShift or Kubernetes
Familiarity with event-driven architectures and Kafka
Domain Experience
Experience working with financial data, ideally in banking or trading environments
Understanding of risk and PnL data structures is a strong advantage
Working Style
Strong ownership mindset with the ability to work independently
Structured, pragmatic, and delivery-focused
Comfortable operating in complex and regulated environments
Clear communicator across both technical and business stakeholders
What Success Looks Like
Within the first months, you will have:
Delivered initial Data Vault structures and migrated datasets into the new platform
Established stable, event-driven pipelines
Ensured data consistency and validation between legacy and new systems
Contributed to a production-ready, scalable data platform
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