We’re DF Capital – a specialist bank providing award-winning commercial finance, retail finance, and savings products to consumers and small businesses.
Based in Manchester, we serve thousands of customers across the UK and into Europe, supporting their ambitions with tailored financial solutions.
We help our customers realise their ambitions by doing things differently – combining the agility and innovation of a specialist lender with the security and service standards of a regulated bank. Whether it’s flexible lending structures or straightforward savings options, we focus on what matters most to our customers.
In 2025, we launched DF Capital Retail Finance – a subsidiary of DF Capital Bank Limited – to offer specialist hire purchase solutions to retail customers.
Our goal is simple: to do the absolute best for our customers, our communities, and each other.
Your new role:
We are seeking a Data Engineer to join our growing Data & Analytics team within a medium-sized UK bank. Working in a small, agile, you will play a key role in sourcing, integrating, transforming and delivering data from a variety of internal and external systems.
You will be responsible for building and maintaining scalable cloud-based data solutions, ingesting structured data from databases and operational systems, as well as semi-structured data from APIs and third-party services. Using Snowflake, DBT and Windmill, you will develop robust data pipelines that support regulatory reporting, operational processes, business intelligence and analytical workloads.
This role is well suited to an engineer who enjoys taking ownership, working across the full data lifecycle, and contributing to the continued development of a modern cloud data platform within a regulated financial services environment.
Requirements
Data Engineering & Development
Design, develop and maintain scalable data pipelines and data integration solutions.
Build and optimise SQL-based solutions within Snowflake.
Create and maintain robust ELT processes supporting reporting and analytics requirements.
Develop and manage data models that support business, operational and regulatory needs.
Ensure data quality, consistency and integrity across multiple data sources.
Document data flows, transformation logic and technical solutions.
Data Integration & Ingestion
Design and maintain ingestion pipelines for both structured and semi-structured data.
Source and integrate data from operational databases, applications and third-party platforms.
Develop integrations with REST APIs and other external data services.
Extract, process and transform semi-structured data formats such as JSON and XML.
Monitor ingestion processes and implement controls to ensure data reliability and completeness.
Work with business and technical stakeholders to understand source systems and data requirements.
Data Transformation & Modelling
Build and maintain transformation layers using SQL and DBT.
Design reusable and scalable data models following engineering best practices.
Implement testing and validation to ensure high levels of data quality.
Optimise transformation performance and maintainability.
Platform Automation & Orchestration
Develop and maintain automated workflows using Windmill.
Schedule, orchestrate and monitor data processing activities.
Identify opportunities to improve operational efficiency through automation.
Support troubleshooting and resolution of production issues.
DevOps & CI/CD
Use GitHub for source control, collaboration and code reviews.
Support and improve CI/CD processes for data platform deployments.
Promote engineering best practices including version control, testing and release management.
Maintain technical documentation and operational runbooks.
Collaboration & Continuous Improvement
Work closely with the Lead Data Engineer and wider stakeholders across the bank.
Participate in architecture discussions and solution design.
Contribute to the evolution of the data platform and engineering…