company_site
Data Engineer
GLAS
London, GBR
Full-time
Mid
Onsite
Discovered Yesterday
azurepower-bi
Key Skills for This Role
azurepower-bi
Full Job Posting
KEY RESPONSIBILITIES
- Fabric migration: Support the migration and modernisation of data pipelines, storage and reporting workloads from Azure Synapse to Microsoft Fabric.
- Data engineering: Build and maintain Fabric Data Factory pipelines, Lakehouses, Warehouses, Notebooks, Dataflows and reusable data products.
- Data architecture: Implement scalable data architectures and engineering patterns, including Bronze, Silver and Gold data layers.
- SQL development: Develop, optimise and troubleshoot SQL and T-SQL used within data pipelines, transformations, warehouses and reporting solutions.
- Semantic modelling: Design and maintain governed Semantic Models and curated datasets for Power BI and downstream consumers.
- Data quality and governance: Implement validation, reconciliation, monitoring and lineage processes to ensure data accuracy, consistency and trust.
- Automation and AI enablement: Support intelligent automation, AI-driven data quality controls and governed AI use cases using trusted enterprise data.
- Fabric administration: Support workspace, capacity, deployment, gateway, monitoring and performance management activities.
- Security: Implement and maintain role-based access controls, row-level security and appropriate data access controls.
- DevOps and release management: Contribute to source control, CI/CD pipelines, automated testing and controlled deployments using Azure DevOps.
- Operational support: Monitor platform performance, investigate issues and maintain technical documentation.
- Stakeholder engagement: Translate business and reporting requirements into scalable and maintainable data solutions.
- MUST-HAVE SKILLS AND EXPERIENCE
- Microsoft Fabric: Hands-on experience with Fabric Data Factory, Lakehouse, Warehouse, OneLake, Notebooks, Dataflows and Semantic Models.
- Fabric migration: Experience supporting or delivering migrations from Azure Synapse to Microsoft Fabric
- Advanced SQL: Strong SQL and T-SQL development, optimisation and troubleshooting skills.
- Python and Spark: Experience building data processing and transformation solutions using Python and Spark.
- Data engineering fundamentals: Strong understanding of ETL/ELT, data warehousing, dimensional modelling, orchestration and production support.
- Power BI and Semantic Models: Experience designing reusable semantic models, reporting datasets and data structures for analytics consumers.
- Data quality and governance: Experience implementing validation, monitoring, lineage, metadata and governed data access controls.
- Fabric administration: Experience managing Fabric workspaces, capacities, security and performance optimisation.
- DevOps: Experience with source control, CI/CD and automated deployment practices.
- Communication: Ability to engage effectively with both technical and non-technical stakeholders.
DESIRABLE SKILLS
- AND EXPERIENCE
- Financial services: Experience working with regulated financial data, finance reporting, debt capital markets, loan administration or similar domains.
- Microsoft AI ecosystem: Experience with Microsoft Copilot, Fabric data agents, Azure AI services, retrieval-augmented generation or related enterprise AI capabilities.
- Data governance: Experience with Microsoft Purview, data cataloguing, master data management and enterprise data-quality frameworks.
- Machine learning and advanced analytics: Experience applying analytical or AI techniques to data quality, anomaly detection or operational automation
- Cost Optimization: Understanding of Fabric capacity management and cost-efficient solution design.
- WHAT SUCCESS LOOKS LIKE
- Synapse workloads are migrated to Microsoft Fabric through controlled, tested and supportable delivery.
- Fabric data products are secure, governed, reusable and performant.
- Data quality and validation controls identify issues earlier and reduce manual reconciliation.
- Semantic Models provide consistent business definitions across Power BI, AI and downstream consumers.
- AI-ready datasets are documented, traceable and suitable for responsible use by Copilot and agentic solutions.
- Agentic workflows and intelligent automation reduce manual effort without weakening governance or control.
- Platform monitoring, documentation and operational support improve reliability and resilience.
About GLAS
Banking450 employeesFounded 2011
GLAS is a privately owned financial-services firm providing loan administration, debt administration, restructuring, and trustee services to credit-market participants.
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