Data Engineer

**About the Business**
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Quilter plc is a leading wealth management business, helping to enable brighter financial futures for every generation.
Quilter oversees £160.5 billion in customer investments (as of September 2026). It has an adviser and customer offering spanning financial advice, investment platforms, multi-asset investment solutions, and discretionary fund management. The business is comprised of two segments: Affluent and High Net Worth.
Affluent encompasses the financial planning business, Quilter Financial Planning, the Quilter Investment Platform and Quilter Investors, the multi-asset investment solutions business.
High Net Worth includes the discretionary fund management business, Quilter Cheviot, together with Quilter Cheviot Financial Planning – offering a highly personalised service to private clients, charities, trustees, and professional partners. Quilter Cheviot has presence throughout the UK, Ireland and Channel Islands.
At Quilter we never stand still. Our foundations are rooted in our extraordinary expertise, which is trusted by hundreds of thousands of customers, but we have great ambitions to stay one step ahead and make an even greater difference to the people and communities we serve, including our colleagues.
Our business is transforming, continually modernising, and becoming even more customer centric. So, if you want to be bold in the pursuit of your ambitions, bring new ideas, and challenge and evolve what we do, it’s the perfect time to join us!
**About the Role**
======================
Level: 4
Department: COO
Location: Southampton or London
Contract type: Permanent
The role sits within the Chief Data Office (CDO) and reports to the Data Engineering Manager, with technical oversight and guidance provided by a Lead Data Engineer.
The Data Engineer is responsible for designing, developing, testing, deploying, and supporting data solutions that enable trusted, timely, and accessible data across the organisation. The role supports Quilter's data strategy by delivering scalable, secure, and resilient data products and services that underpin reporting, analytics, regulatory obligations, operational processes, and digital capabilities.
Working within Agile delivery teams, the Data Engineer collaborates with Product Owners, Business Analysts, Data Analysts, Architects, platform teams, and business stakeholders to transform data requirements into high-quality engineering solutions. The role contributes to the continuous evolution of Quilter's cloud-based data platform, leveraging technologies including Azure Data Factory, Azure Data Lake Storage, Databricks, Azure Key Vault, Azure DevOps, and related Azure services.
The successful candidate will demonstrate strong technical capability, a proactive approach to problem solving, a commitment to engineering excellence, and a focus on delivering business value through data.
Key Accountabilities
Data Solution Development
* Design, develop, test, and maintain data pipelines, integrations, and data products across the enterprise data platform.
* Build scalable and reusable solutions for data ingestion, transformation, enrichment, and consumption.
* Develop and optimise batch, near real-time, and streaming data processing solutions where required.
* Ensure solutions are robust, performant, secure, and maintainable.
* Contribute to the delivery of analytical, operational, and regulatory data capabilities.
Data Platform Engineering
* Develop and maintain solutions using Azure Data Factory, Azure Data Lake Storage, Databricks, Delta Lake, Azure Key Vault, and related Azure technologies.
* Support the ongoing enhancement of platform capabilities, standards, frameworks, and reusable components.
* Implement infrastructure and configuration changes through automated deployment processes.
* Contribute to platform modernisation initiatives and technology adoption activities.
Engineering Standards & Quality
* Follow established engineering standards, coding practices, and architectural principles.
* Maintain source control and deployment processes using Azure DevOps and Git-based workflows.
* Create and maintain automated testing and validation processes.
* Participate in code reviews and peer reviews to promote quality, consistency, and knowledge sharing.
* Ensure solutions are appropriately documented and supportable.
Data Governance, Security & Risk Management
* Apply data governance, security, privacy, and retention requirements throughout the engineering lifecycle.
* Ensure data solutions comply with internal policies, regulatory obligations, and security standards.
* Support implementation of role-based access controls, data classification requirements, and auditability standards.
* Contribute to maintaining accurate metadata, lineage, and data quality controls.
* Identify and escalate operational, security, and data risks where appropriate.
Operational Support & Reliability
* Monitor and support production data products and services.
* Investigate and resolve incidents, defects, and performance-related issues.
* Participate in root cause analysis activities and implement preventative improvements.
* Support service resilience through monitoring, alerting, automation, and operational best practices.
* Contribute to capacity planning, performance tuning, and cost optimisation activities.
Stakeholder Collaboration
* Work closely with Product Owners, Analysts, Architects, Platform Engineers, and business stakeholders to understand requirements and priorities.
* Interpret business requirements and translate them into technical solutions.
* Provide updates on delivery progress, risks, issues, and dependencies.
* Support user adoption by ensuring solutions are reliable, well-documented, and aligned with business needs.
Continuous Improvement & Innovation
* Identify opportunities to improve engineering practices, tooling, automation, and delivery processes.
* Stay current with developments across Azure, Databricks, data engineering, and cloud technologies.
* Contribute ideas and recommendations to improve platform efficiency, reliability, and scalability.
* Participate in knowledge sharing, technical communities, and continuous learning activities.
**About You**
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Qualifications
· Degree in a technical discipline (e.g. Computer Science, Engineering, Mathematics, Physics) or evidence of equivalent practical experience.
· Relevant industry certifications in Azure, Databricks, Data Engineering, or cloud technologies are desirable but not essential.
Knowledge & Experience
·Proven experience as a Data Engineer delivering data pipelines, data integrations, and data products within a cloud-based enterprise environment.
·Hands-on experience designing, developing, testing, and supporting data solutions using Databricks within Azure.
·Experience developing batch and near real-time data processing solutions using modern data engineering technologies and patterns.
·Experience building and maintaining scalable, reliable, and secure data pipelines that support reporting, analytics, data science and operational use cases.
·Understanding of data quality, validation, monitoring, and observability concepts and their application within data engineering solutions.
·Knowledge of data governance principles, including data lineage, metadata management, security, and access control practices.
·Experience working within regulated environments and applying data protection, information security, and data retention requirements.
·Experience applying engineering standards and development best practices including source control, testing, automation, and CI/CD.
·Experience contributing to code reviews, peer reviews, and collaborative engineering activities.
· Knowledge of data modelling concepts, including dimensional modelling, lakehouse architectures, and modern data platform