Fabric Data Engineer — Workplace Engineering

About the Role 

Vanguard is standing up Microsoft Fabric as the enterprise data and analytics foundation that powers our Workplace AI, Power BI, and cross-cloud analytics estate. We are partnering with Microsoft on a CDAO-led Fabric Enablement engagement and are building this capability on an F256 Reserved capacity, integrated with the broader Vanguard data, identity, and security stack — including OneLake Direct Lake against AWS S3, Entra ID and Okta federation, and Microsoft Purview. 

Role Summary

We are hiring a hands-on Fabric Data Engineer to own the data layer of that capability. This is a builder's role, not an architect-only role. The engineer designs and implements scalable data products in OneLake — lakehouses, warehouses, pipelines, notebooks, semantic-model-ready Delta tables — and is accountable for the lifecycle, governance, and operational health of the Fabric platform. The complementary AI Engineer role consumes that foundation to build agents, copilots, and Foundry orchestrations; this engineer makes sure the data underneath is governed, monitored, and ready. 

You will partner closely with the AI Engineer on AI-ready data products and semantic-layer handoffs; with our Technical Project Manager on program delivery, enablement, and change management; and with our Cloud Domain Architect on platform alignment. You will work alongside the Microsoft CDAO Fabric Enablement team and Vanguard partners across CDAO and Workplace Engineering. You will be a core member of the emerging Workplace AI Fusion Team. This is a strategic engineering and implementation role, not a support position. 

Key Responsibilities (Fabric Build & Data Engineering) 

• Design and implement scalable data storage in OneLake using Lakehouses (Delta) and Warehouses (T-SQL); choose the right item for each workload and configure SQL analytics endpoints, shortcuts, and OneLake security. 

• Build and maintain Spark notebooks (PySpark), Data Factory pipelines, Dataflows Gen2, Copy Jobs, and mirroring for batch and incremental ingestion at enterprise scale. 

• Build Real-Time Intelligence solutions: Eventstreams, Eventhouses / KQL databases, Activator reflexes, and Spark structured streaming for low-latency workloads. 

• Optimize Lakehouse tables (OPTIMIZE, V-Order, Z-Order, partitioning) and Direct Lake semantic-model-ready datasets so downstream Power BI and AI agents perform predictably. 

ALM & Lifecycle Engineering 

• Implement source control, branching, and CI/CD using native Fabric Git integration (Azure DevOps and GitHub), Fabric Deployment Pipelines, and the Microsoft fabric-cicd Python library. 

• Automate Dev / Test / Prod promotion against the Fabric REST API using service principals and Workload Identity Federation; codify environment-aware bindings via Variable Libraries and parameter.yml. 

• Operate a Feature → Dev → UAT → Prod branching pattern — native Git on Feature and Dev workspaces, pipeline-pushed promotion to UAT and Prod — with mandatory PR review, cherry-pick promotion, and one repo per team to scope blast radius. 

• Own the lifecycle of Fabric data components from creation through retirement, ensuring every environment is reproducible from the GitHub pipeline rather than from the Fabric UI. 

Platform Operations & Monitoring 

• Operate the Fabric F256 capacity: monitor CU consumption with the Capacity Metrics App, manage smoothing windows, diagnose interactive and background throttling, and right-size workloads. 

• Build telemetry using the Monitoring Hub, per-workspace Workspace Monitoring (Eventhouse-based KQL logs), Eventhouse monitoring, and the Admin Monitoring Workspace to surface refresh failures, pipeline errors, and semantic-model health. 

• Define dashboards and alerts for ingestion,