Product Engineering (Fabric Data Engineering)

At Allstate, great things happen when our people work together to protect families and their belongings from life’s uncertainties. And for more than 90 years, our innovative drive has kept us a step ahead of our customers’ evolving needs. From advocating for seat belts, air bags and graduated driving laws, to being an industry leader in pricing sophistication, telematics, and, more recently, device and identity protection. 

The Product Engineering (Fabric Data Engineering) role combines platform engineering, data engineering, dimensional modeling, Gold-layer delivery, and AI-augmented analytics enablement within Microsoft Fabric. This role is accountable for designing and operating the team’s Fabric workspace, OneLake/Lakehouse architecture, CI/CD release process, Bronze ingestion patterns, Silver/Gold transformation pipelines, Delta table performance strategy, security model, and observability framework. The role also owns the end-to-end data product lifecycle from raw ingestion through trusted dimensional models and Power BI semantic models, ensuring Auto and Property insurance data is accurate, reusable, performant, governed, and ready for business self-service, regulatory reporting, Copilot-assisted analytics, and downstream application/API consumption.

• Design /Code and own the Microsoft Fabric workspace, OneLake, Lakehouse, and Warehouse architecture, including Bronze-layer ingestion patterns for policy, claims, billing, and third-party vendor data feeds.
• Establish and enforce Fabric data build standards, governance practices, reusable engineering patterns, naming conventions, and code-review expectations across the delivery team.
• Build and maintain Azure DevOps CI/CD pipelines across development, test, and production environments, including approval gates, service principal management, deployment parameterization, and repeatable release processes.
• Design, build, and optimize Silver-layer cleansing, deduplication, conformance, and reconciliation logic to create trusted, audit-ready datasets for downstream analytics.
• Design and maintain Gold-layer dimensional models, star schemas, Slowly Changing Dimension Type 2 history tracking, incremental load patterns, and business-ready marts using Kimball and analytics-engineering principles.
• Implement automated data-quality controls, including row-count validation, measure reconciliation, SCD2 integrity checks, lineage validation, and exception reporting.
• Optimize Delta tables and Fabric workloads using techniques such as OPTIMIZE, Z-Order, V-Order, Liquid Clustering, VACUUM, Change Data Feed, and Spark resource tuning based on actual workload and Power BI query patterns.
• Own the Fabric and OneLake security model, including workspace roles, OneLake RBAC, row-level security, column-level security, object-level security, Microsoft Purview sensitivity labels, and DLP alignment.
• Design, build, and maintain Power BI Direct Lake semantic models, DAX measures, dataset access controls, and performance-tuned reporting layers directly on top of the Gold layer.
• Configure semantic models for AI and Copilot readiness, including AI data schemas, verified answers, clear table/column/measure descriptions, and business-friendly metadata.
• Use Copilot in Power BI and engineering productivity tools such as GitHub Copilot to accelerate modeling, report creation, documentation, and validation while ensuring all generated outputs meet enterprise quality standards.
• Establish operational monitoring and observability using Fabric Capacity Metrics, Monitoring Hub, workspace monitoring, Azure Monitor, and Log Analytics to proactively identify performance or reliability issues.
• Troubleshoot production pipeline, data-quality, semantic model, and report-performance issues; lead root-cause analysis and coordinate remediation with platform, analytics, business, and support teams.
• Expose Lakehouse, Warehouse, Gold-layer, or semantic-model data through Fabric API for GraphQL or REST where programmatic or application-level consumption is required.
• Maintain architecture documentation, runbooks, lineage definitions, business glossaries, onboarding materials, and self-service guidance for technical and non-technical stakeholders.
• Partner with business stakeholders across underwriting, claims, actuarial, finance, and regulatory reporting teams to translate data requirements into scalable Fabric data products and explain analytical outputs in business terms.
• Mentor engineers, review pull requests, support capacity planning, and promote shared engineering practices across data engineering, analytics engineering, and Power BI development activities.

• Microsoft Fabric engineering, including Workspaces, OneLake, Lakehouse, Warehouse, Notebooks, Data Pipelines, Dataflows, and Direct Lake.
• Data engineering and transformation development using SQL, PySpark, Python, Spark, Delta Lake, and modern ELT patterns.
• Bronze, Silver, and Gold Lakehouse architecture, including ingestion, cleansing, conformance, reconciliation, and business-ready data mart design.
• Dimensional modeling expertise, including Kimball methodology, star schemas, fact/dimension design, grain definition, Slowly Changing Dimensions Type 2, and incremental load patterns.
• Delta table performance optimization using OPTIMIZE, Z-Order, V-Order, Liquid Clustering, VACUUM, Change Data Feed, partitioning strategy, and Spark workload tuning.
• Azure DevOps and CI/CD skills, including Git-based development, pull requests, approval gates, environment promotion, deployment automation, and release governance.
• Power BI semantic model development, including Direct Lake models, relationships, DAX measures, calculation logic, dataset security, and report performance optimization.
• AI and Copilot readiness for analytics, including semantic model descriptions, verified answers, AI data schemas, business-friendly metadata, and validation of AI-generated outputs.
• Data governance and security, including OneLake RBAC, workspace roles, row-level security, column-level security, object-level security, Microsoft Purview sensitivity labels, lineage, and DLP alignment.
• Data quality engineering, including automated validation, reconciliation checks, exception handling, lineage validation, auditability, and regulatory reporting support.
• Monitoring and operational support using Fabric Capacity Metrics, Monitoring Hub, workspace monitoring, Azure Monitor, Log Analytics, incident triage, and root-cause analysis.
• Insurance domain understanding, especially Auto and Property policy, claims, billing, actuarial, underwriting, regulatory reporting, loss ratio, and combined ratio concepts.
• API and integration awareness, including Fabric API for GraphQL, REST-based consumption patterns, and governed downstream application access.
• Documentation and communication skills, including architecture documentation, runbooks, business glossaries, data lineage, onboarding materials, and stakeholder-ready explanations.
• Technical leadership skills, including mentoring engineers, reviewing code, defining reusable patterns, enforcing standards, supporting capacity planning, and collaborating across platform, analytics, and business teams.

Allstate Canada Group has policies and practices that provide workplace accommodations. If you require accommodation, please let us know and we will work with you to meet your needs. 

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Allstate Canada Group uses AI technology tools to assist in screening, selecting, assessing, and scheduling interviews with candidates as part of the recruitment process.

This job posting is for a current open role within the organization.