Hybrid Fabric Data Engineer for AI-Ready Platform

GLAS; City Of London; Hybrid; GBP 70,000 - 110,000; Full time

Benefits offered by this job

Base salary + bonus

Hybrid working

28 days annual leave + bank holidays

Long service award

Private Medical Insurance

Private pension plan

Life insurance plan

Employee Assistance Program (EAP)

Season ticket loan scheme

Yearly eye examination

Gym membership discount

Career development and study support

Job summary

GLAS is seeking a data engineer to lead the migration of enterprise data workloads from Azure Synapse to Microsoft Fabric. You will design and implement Fabric Data Factory pipelines, lakehouse and semantic models to power reporting, analytics and AI-enabled capabilities.

Working with stakeholders across finance and tech, you will ensure data quality, governance and scalable data architectures while following GLAS security standards.

Qualifications

• Hands-on experience with Fabric Data Factory, Lakehouse, Warehouse, OneLake, Notebooks, Dataflows and Semantic Models.
• Experience supporting or delivering migrations from Azure Synapse to Microsoft Fabric
• Strong SQL and T-SQL development, optimisation and troubleshooting skills.
• Experience building data processing and transformation solutions using Python and Spark.
• Strong understanding of ETL/ELT, data warehousing, dimensional modelling, orchestration and production support.
• Experience designing reusable semantic models, reporting datasets and data structures for analytics consumers.
• Experience implementing validation, monitoring, lineage, metadata and governed data access controls.
• Experience managing Fabric workspaces, capacities, security and performance optimisation.
• Experience with source control, CI/CD and automated deployment practices.
• Ability to engage effectively with both technical and non-technical stakeholders.

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.

Skills

Microsoft Fabric

Fabric migration

Advanced SQL

Python and Spark

Data engineering fundamentals

Power BI Semantic Models

Data quality governance

Fabric administration

DevOps

Communication

Tools

Azure Synapse

Microsoft Fabric

Job description

GLAS is seeking a data engineer to lead the migration of enterprise data workloads from Azure Synapse to Microsoft Fabric. You will design and implement Fabric Data Factory pipelines, lakehouse and semantic models to power reporting, analytics and AI-enabled capabilities.

Working with stakeholders across finance and tech, you will ensure data quality, governance and scalable data architectures while following GLAS security standards.