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, transformation, refresh, and capacity health; drive root-cause analysis on production incidents and feed lessons back into platform standards. 

• Define and operate the on-call model for production data pipelines and Fabric items in partnership with Tier 3 Engineering. 

Standards, Governance & Security 

• Define and enforce Fabric platform standards through Terraform-based IaC using the official microsoft/fabric provider (workspaces, capacities, domains, items), workspace templates, naming and tagging conventions, and automated CI policy checks against the Fabric REST API. 

• Manage tenant settings, domains, and capacity allocation in partnership with the Fabric Center of Excellence; align identity with Entra ID and Okta federation; rotate service principals and use PIM for elevated admin roles. 

• Implement RBAC patterns that separate workspace control-plane roles (Admin / Member / Contributor / Viewer) from OneLake data-plane roles (folder and table level); operate RLS, CLS, OLS, dynamic data masking, and item-level sharing. 

• Integrate Microsoft Purview for sensitivity labels, DLP, metadata scanning, lineage, and impact analysis; manage endorsement (Promoted / Certified) so AI agents and BI consumers only ground on trusted datasets. 

Integration & Interoperability 

• Build cross-cloud integration patterns: OneLake Direct Lake against AWS S3, Mirrored Databases for Snowflake, SQL Server, and Cosmos, and shortcuts that avoid Athena and ODBC where Direct Lake delivers better performance. 

• Publish governed, AI-ready data products with Prep for AI configured on semantic models so Fabric Data Agents, Copilot Studio, and Azure AI Foundry can ground on certified Vanguard data. 

• Coordinate with Data, Cloud, Identity, and Security domain teams on data-sharing patterns, private link configuration, and on-prem data gateway operations across the current 6–8 gateway footprint. 

Tier 3 Escalation & Expert Support 

• Serve as Tier 3 escalation for complex Fabric, OneLake, pipeline, capacity, and Direct Lake issues across the enterprise. 

• Provide deep technical consultation to Workplace Engineering, CDAO, and partner teams onboarding workloads to Fabric. 

• Build reusable patterns, reference implementations, and internal playbooks for ingestion, modeling, deployment, and capacity operations that scale beyond a single engineer. 

Innovation & Strategic Oversight 

• Lead proof-of-concept work for new Fabric capabilities (Mirrored Databases, GraphQL APIs, the SQL Database item, Real-Time Intelligence enhancements, Fabric MCP integration, evolving Direct Lake and Prep-for-AI features). 

• Partner with the Microsoft CDAO Fabric Enablement engagement to bring product roadmap insights back into Vanguard's implementation. 

• Contribute to the Workplace AI and enterprise Data roadmap and operating model, and partner with champions and train-the-trainer initiatives to translate engineering work into adoption outcomes. 

Required Qualifications and Skills

• 8+ years of professional software / data / platform engineering experience, with 5+ years building production data solutions on the Microsoft and / or Azure data stack. 

• Hands-on production experience with at least three of: Microsoft Fabric (Lakehouse, Warehouse, Pipelines, Notebooks, Real-Time Intelligence), Azure Synapse, Azure Data Factory, Databricks, Power BI semantic models, Azure SQL / SQL Server. 

• Strong skills in SQL, PySpark, and KQL — the core Fabric language trio — and comfort moving between batch, streaming, and interactive analytics workloads. 

• Demonstrable experience designing and shipping CI/CD for data platforms: Git workflows, automated deployment, environment promotion, secret-less authentication, and infrastructure-as-code. 

• Working knowledge of Terraform (preferred) or Bicep for cloud platform automation, including provider versioning, state management, and policy-as-code patterns. 

• Experience implementing security and compliance controls in a regulated environment: Purview, Sentinel, Defender, Conditional Access, MIP, DLP, RBAC, RLS / CLS / OLS, dynamic data masking. 

• Identity fluency with Entra ID (Azure AD) and federated IdPs (Okta preferred); experience with service principals, managed identities, and Workload Identity Federation. 

• Experience working in financial services, healthcare, or another heavily regulated environment, or a credible plan to come up to speed quickly. 

• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience. 

Preferred Attributes 

• DP-700 (Microsoft Certified: Fabric Data Engineer Associate) required or in-progress within 6 months of hire; DP-600 (Fabric Analytics Engineer Associate) and AZ-305 (Azure Solutions Architect Expert) preferred. 

• Hands-on experience with the Microsoft fabric-cicd Python library and the microsoft/fabric Terraform provider. 

• Experience operating a Fabric Center of Excellence, Power BI CoE, or comparable data-platform CoE. 

• Experience with cross-cloud data integration patterns (OneLake ↔ AWS S3, mirroring, shortcuts) and BCDR for analytics platforms at enterprise scale. 

• Experience configuring Prep for AI on semantic models and partnering with AI / agent engineers on certified data-product handoffs. 

• Background contributing to internal communities of practice, champions networks, or developer enablement programs. 

• Prior experience as a hands-on engineer in a Fusion Team (engineers + product + data + analysts) or Data / AI Center of Excellence model. 

• Additional vendor certifications welcomed but not required: AZ-204, SC-100, DP-203 (legacy, retired March 2025 but still relevant context).