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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - **Company:** Nucleus Financial - **Location:** UK (Remote available) - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Agile Methodology, Artificial Intelligence, Business Analytics Applications, Audit Trail, Microsoft Azure, C Sharp (Programming Language), Code Review, Information Engineering, Extract Transform Load (ETL), Data Warehousing, Database Connection, Database Development, Database Queries, Dimensional Modeling, Python (Programming Language), Microsoft SQL Server, SQL Azure, Windows PowerShell, Power BI, SQL Stored Procedures, Data Logging, File Transfer Protocol (FTP), Cloud Platform System, Azure Data Factory, Delivery Pipeline, Git, Microsoft Fabric, Azure Synapse Analytics, Software Version Control, Data Pipelines, Databricks - **Published:** September 21, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=35a8885718199122 ## About the Role Nucleus Financial Platforms runs its data estate on Microsoft Fabric. The Data Engineering team ingests data from the group's platform systems and enterprise tools, transforms it through a four layer architecture, and delivers it as a curated data warehouse and semantic model layer. That output supports management information, financial reporting, self service analytics, an emerging AI capability, and regulatory returns under FCA and PRA obligations., * SQL development: Strong SQL skills, including writing and optimising stored procedures against a relational warehouse such as SQL Server, Azure SQL or Fabric. * ETL and pipeline development: Practical experience building, troubleshooting and maintaining ETL processes and data pipelines in a cloud data platform such as Microsoft Fabric, Azure Data Factory, Synapse or Databricks. * Dimensional modelling: A solid grounding in data warehousing concepts, including star schemas, fact grain, surrogate keys and slowly changing dimensions. * Semantic models and reporting: Experience building semantic models, writing DAX measures, and developing Power BI reports or dashboards. * Python and scripting: Working knowledge of Python for file parsing and automation, ideally within notebooks. PowerShell or C# are useful additions. * Source control and peer review: Comfortable with Git branching, pull requests and code review, ideally in Azure DevOps. * Quality and testing: A methodical approach to validating your own work and that of your peers, including reconciling back to source and checking downstream impact. * Analytical and problem solving skills: The ability to trace a data issue back through the layers to its cause rather than patching the symptom. * Communication: Able to explain technical detail to non technical colleagues, and to ask the right questions when a requirement is not fully formed. Desirable Experience: * Microsoft Fabric: Hands on experience with the Fabric Lakehouse, Fabric Data Warehouse, Data Pipelines, Notebooks and deployment pipelines. A certification such as DP-700 is welcome but not required. * Regulated environment: Experience working under formal change control, where evidence and auditability are attached to what you deliver. * Financial services domain: Exposure to platform, wrap, pensions or investment data, including holdings, transactions, flows and charges. * Data quality and reconciliation: Experience operating or designing reconciliation and validation controls between a source system and a warehouse. ## Description As a Data Engineer you will build and maintain the pipelines, warehouse objects and reporting models that make that happen. You will work within an established architecture and a documented set of design standards, taking individual pipelines and warehouse objects from development through peer review and into production. The platform holds client and financial data in a regulated environment, so accuracy, repeatability and a clear audit trail matter as much as delivery speed. How we work We work in an Agile cycle alongside a Product Owner. Everything we build is version controlled in Azure DevOps and promoted through Fabric deployment pipelines across development, test and live. Development takes place in an isolated feature workspace, every change is peer reviewed before it reaches production, and anything touching regulatory reporting carries an additional test and approval gate. A daily rota covers platform health checks each morning, and documentation is treated as part of the build rather than an afterthought. Responsibilities: * Build and maintain data pipelines: Develop Fabric Data Pipelines and Notebooks that collect data from platform and enterprise source systems over SFTP, API and gateway database connections, land it in the Fabric Lakehouse, and load it into the persistent staging layer of the Fabric Data Warehouse. * Develop warehouse logic: Write and optimise the SQL stored procedures that transform staged data into the curated dimensional model, applying the team's standard dimension and fact load patterns. * Build the consumption layer: Develop Fabric semantic models and DAX measures, Power BI reports and paginated reports that give the business governed, self service access to the warehouse. * Work to the platform design standards: Apply the documented architecture, naming conventions and re-runnability rules to everything you build, so that any step can be safely re-run from the point of failure without producing duplicate or inconsistent data. * Test and validate your own work: Run affected pipelines end to end in an isolated feature workspace, and check row counts, key values, logging and downstream impact against expected results before raising a pull request. * Review your colleagues' work: Carry out peer review on pull requests in Azure DevOps, checking correctness, adherence to standards, and the impact of a change on shared objects and downstream reports. * Support daily platform operations: Take your turn on the ETL rota, confirming overnight runs have completed, investigating failures, and applying the documented recovery procedures within the team's resolution targets. * Work within change control: Use feature branches and feature workspaces for every change, and meet the additional approval gates that apply to regulatory pipelines and semantic models. * Document what you build: Produce and maintain per pipeline documentation covering transformation logic, data mappings and known edge cases, so that any engineer in the team can operate and recover your pipelines. * Work with the business: Collaborate with colleagues across finance, operations, risk and the platform businesses to understand what they need from the data, and help them interpret what they are given. * Take part in Agile ceremonies: Contribute to planning, backlog refinement and retrospectives, and keep the team sighted on progress and blockers. ## Related Videos - [Data Analytics with Microsoft Fabric: End-to-End Use Case with Data Agents](https://www.wearedevelopers.com/videos/1547-data-analytics-with-microsoft-fabric-end-to-end-use-case-with-data-agents) - [Hacking MSSQL on Cloud. 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