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
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Translate legacy ETL logic from SSIS and stored procedures into modern ELT pipelines using dbt
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Implement Data Vault 2.0 structures including Raw Vault and Business Vault
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Build datamarts and curated datasets for downstream analytics and reporting
Orchestration & Infrastructure
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Design and operate workflows using Dagster, including scheduling, dependencies, and recovery mechanisms
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Deploy and run data workloads on OpenShift / Kubernetes environments
Event-Driven Data Processing
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Enable near real-time data processing using Kafka-triggered pipelines
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Integrate with upstream data lake environments and external data providers
Data Quality & Validation
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Establish robust data validation and reconciliation processes
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Implement automated testing and monitoring using dbt
Operational Ownership
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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
Requirements
What You Bring
Technical Expertise
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Strong experience with SQL Server and T-SQL, including performance optimisation
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Proven hands-on experience with dbt in production environments
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Solid experience with workflow orchestration tools, ideally Dagster
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Practical knowledge of Data Vault 2.0 modelling concepts
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Experience working with container platforms such as OpenShift or Kubernetes
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Familiarity with event-driven architectures and Kafka
Domain Experience
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Experience working with financial data, ideally in banking or trading environments
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Understanding of risk and PnL data structures is a strong advantage
Working Style
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Strong ownership mindset with the ability to work independently
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Structured, pragmatic, and delivery-focused
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Comfortable operating in complex and regulated environments
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Clear communicator across both technical and business stakeholders
What Success Looks Like
Within the first months, you will have:
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Delivered initial Data Vault structures and migrated datasets into the new platform
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Established stable, event-driven pipelines
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Ensured data consistency and validation between legacy and new systems
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Contributed to a production-ready, scalable data platform
