The role
We are looking for an experienced Data Engineer to join our IT team and play a key role in the development of our firmwide data capability.
This is a newly created position and our first dedicated Data Engineering hire, joining at an important stage in the development of Gateley’s wider data strategy. We are building the foundations that will allow us to bring together data that currently sits across different systems and areas of the business, improve its quality and governance, and make it easier to use for insight, reporting, analytics and emerging AI capabilities.
You will have the opportunity to get involved at the ground level of that journey. Working predominantly within Microsoft Fabric, you will help us unlock data from existing sources, bring it into our developing data platform and engineer the pipelines and structures needed to cleanse, consolidate and transform it into trusted, reusable data products. A key part of this will be helping us develop reliable "golden records" and stronger master data practices, providing a trusted foundation for dashboards, analytics and future AI-enabled use cases.
The scope is deliberately broad. You will combine hands-on engineering with problem solving, technical advisory work and stakeholder engagement, working across IT, Change and different business areas to understand their data, identify priorities and turn requirements into practical solutions.
As the first Data Engineer in a growing capability, you will also have the opportunity to influence how we do things. You will help establish repeatable engineering patterns, standards and ways of working that can support the Data Programme as it develops.
Responsibilities:
- Design, build and maintain data pipelines using Microsoft Fabric, creating reliable ingestion and transformation processes that move data from source systems into governed, reusable data products.
- Develop our Fabric data architecture, creating scalable lakehouse, warehouse and medallion-style structures in line with agreed architecture, governance and security standards.
- Onboard and integrate data from across the business, working with structured and semi-structured information held within core business applications, SaaS platforms, databases, files and approved APIs.
- Build appropriate batch and near-real-time integrations, selecting the right engineering approach based on the underlying business requirement rather than taking a one-size-fits-all approach.
- Help unlock and consolidate data that currently sits across different systems and areas of Gateley, cleansing, transforming and structuring it so that it can be used more effectively for reporting, analytics, decision-making and future AI use cases.
- Ensure production data services are reliable and supportable, implementing appropriate monitoring, logging, exception handling, data lineage and operational support.
- Work directly with application owners, suppliers and internal stakeholders to identify and resolve issues relating to source data, connectivity, schemas, data quality and performance.
- Support the development of master and reference data capabilities, including data matching, entity resolution, identifiers, survivorship and golden-record processes, helping Gateley establish more trusted and consistent information across the organisation.
- Turn data governance and stewardship requirements into practical engineering solutions, including validation rules, quality controls, remediation processes and appropriate workflows.
- Create clear technical and operational documentation, including data definitions, mappings and lineage, ensuring the solutions you develop can be understood and supported by data stewards, technical teams and business stakeholders.
- Provide technical guidance on analytics and Power BI, advising on semantic models, measures, dashboard structure, visual selection, accessibility and performance.
- Help establish consistent definitions and trusted sources of data, ensuring Power BI outputs and other reporting products make use of governed data products rather than recreating business rules and logic locally.
- Challenge requirements constructively, working with stakeholders to understand the problem they are trying to solve and identifying whether a dashboard, metric, alert, workflow or another data product offers the most effective solution.
- Support the development of Fabric agents and AI-enabled data experiences, helping establish the data products, grounding approaches, permissions and access controls required to use these technologies responsibly.
- Assess whether data is ready for AI and agent-based use cases, considering data quality, metadata, access permissions, semantic consistency, governance and traceability before solutions are progressed.
- Work collaboratively with colleagues responsible for AI and data governance, ensuring experimentation remains controlled, transparent and aligned with Gateley’s wider governance and security requirements.
- Apply strong engineering and DevOps practices, including source control, peer review, automated testing, deployment pipelines and appropriate separation of development, test and production environments.
- Work closely with technical and non-technical stakeholders across Gateley, translating complex engineering concepts into straightforward language and helping colleagues understand how data can address their business challenges.
- Take ownership of data engineering solutions from requirement through to delivery, balancing pace with data quality, security, reliability, scalability and long-term maintainability.
- Help establish the standards and ways of working for Gateley’s developing data capability, contributing reusable engineering patterns, templates and best practice that can be adopted as the Data Programme grows. As the first dedicated Data Engineer hire, the successful person will have the opportunity to influence how this capability develops rather than simply inheriting an established engineering model.
The team
Gateley have a dynamic and collaborative IT team, bringing together specialists across IT Service, Infrastructure, Architecture, Security, Change Management, Technology Innovation & Delivery and other technology disciplines.
Our team works across the business to deliver secure, practical and forward-thinking technology solutions that support how our people work today, while helping Gateley prepare for the future.
As our data capability continues to develop, this role will work closely with colleagues across IT and the wider business, providing the opportunity to collaborate with a broad range of technical and non-technical stakeholders.
The person
We are looking for a hands-on Data Engineer who combines strong technical capability with a pragmatic and curious approach to problem solving.
You will be comfortable taking a requirement from initial conversation through to a working technical solution, taking ownership of delivery along the way. You will understand that effective data engineering goes beyond moving data between systems, with a strong focus on data quality, governance, security, scalability and maintainability, as well as how the end product will ultimately be used.
As our data capability continues to develop, you will be comfortable working in an evolving environment and helping to establish effective engineering practices, standards and ways of working. You will enjoy the opportunity to help shape a growing capability rather than stepping into something that is already fully established.
Strong communication skills will be equally important. You will work with both technical and non-technical stakeholders across the business, so you will need to be able to translate complex data and engineering concepts into clear, straightforward language and build effective relationships with people at all levels.
We are open-minded about sector background. You do not need previous legal-sector experience, although experience gained within a corporate, professional services or similarly complex environment would be beneficial.
Essential:
- Strong, hands-on data engineering experience, including building and operating production-grade data pipelines, transformations and curated data products.
- Practical experience with Microsoft Fabric or closely related Microsoft Azure data services, with the ability to quickly become productive across Fabric workloads.
- Strong SQL skills and practical experience with at least one engineering language, typically Python or PySpark.
- Experience integrating data from a range of sources, including enterprise applications, APIs, databases and file-based sources.
- A good understanding of lakehouse, warehouse and dimensional modelling patterns, alongside semantic models and data quality controls.
- Experience applying effective DataOps and DevOps practices, including Git/source control, automated testing, deployment pipelines and environment management.
- Experience designing and delivering data solutions with quality, reliability, security, scalability and maintainability in mind.
- The ability to work with data owners and business stakeholders to translate governance and stewardship requirements into practical engineering controls.
- Strong problem-solving skills, with the ability to take ownership of technical challenges from initial requirement through to delivery.
- Excellent stakeholder communication skills, including the ability to explain complex technical concepts clearly and confidently to non-technical audiences.
- A collaborative approach and the confidence to work across multiple teams and business areas, building effective relationships with both technical and non-technical stakeholders.
Desirable:
It would be advantageous if you have:
- Hands-on experience with Microsoft Fabric, including Data Factory, notebooks, lakehouses, warehouses, Real-Time Intelligence or Fabric agents.
- Experience implementing master data management, entity resolution, reference data or golden-record capabilities.
- Power BI experience, including semantic modelling, DAX, dashboard performance and effective data visualisation.
- Knowledge of Microsoft Purview, including data lineage, metadata, sensitivity classification and data access controls.
- Experience working with established data governance, stewardship or data ownership frameworks.
- Exposure to AI-enabled data products or agents, particularly where data quality, permissions, grounding, traceability and governance are important.
- Experience working within a corporate, professional services, financial services or other information-intensive or regulated environment.
- A relevant Microsoft certification, such as Fabric Data Engineer Associate, or equivalent demonstrable technical expertise.