Senior Data Engineer

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 Open Shift.

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 work streams and deliver reliably in a regulated, high‑stakes environment.

You will play a central role in the end‑to‑end migration and modernisation of the data platform.

• 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

• Design and operate workflows using Dagster, including scheduling, dependencies, and recovery mechanisms
• Deploy and run data workloads on Open Shift / Kubernetes environments

• Enable near real‑time data processing using Kafka‑triggered pipelines
• Integrate with upstream data lake environments and external data providers

• Establish robust data validation and reconciliation processes
• Implement automated testing and monitoring using dbt

• Support production pipelines and resolve incidents when required
• Create clear documentation and ensure operational readiness
• Continuously improve performance, reliability, and maintainability

• 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 Open Shift or Kubernetes
• Familiarity with event‑driven architectures and Kafka

• Experience working with financial data, ideally in banking or trading environments
• Understanding of risk and PnL data structures is a strong advantage

• 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

• 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