Lead Fabric Analytics Engineer

Role & Responsibilities Overview

• Architect and implement end‑to‑end data solutions using Microsoft Fabric components including Data Factory, Lakehouse, Warehouse, Real‑Time Analytics, and OneLake.
• Design scalable data models, integration patterns, and storage strategies to support insurance datasets such as policy, claims, billing, actuarial, and customer information.
• Create architectural blueprints, reference architectures, and reusable frameworks that align with enterprise engineering and cloud standards.
• Develop and manage ingestion pipelines using Fabric Data Factory, enabling batch, real‑time streaming, and API‑based data integrations.
• Build Lakehouse‑centered architectures to support advanced analytics, reporting, and machine learning workloads.
• Optimize data pipelines for performance, reliability, and cost efficiency across Azure and Fabric environments.
• Collaborate with Data Governance teams to ensure adherence to enterprise data standards, lineage tracking, metadata management, and data quality rules.
• Implement governance and compliance capabilities within Fabric, including cataloging, sensitivity labeling, and granular access control.
• Support regulatory requirements for insurance data, ensuring compliance with NAIC guidelines, state-level regulations, and data privacy laws.
• Partner with cloud engineering and security teams to optimize Fabric deployments integrated with Azure AD, Microsoft Purview, and Power BI.
• Ensure architectural alignment with enterprise cloud strategy, security frameworks, and operational guardrails.
• Evaluate new Microsoft Fabric features, capabilities, and roadmap items, recommending adoption and modernization strategies.
• Collaborate with business and analytics stakeholders to translate domain requirements into robust, scalable data solutions.
• Provide technical direction and architectural guidance to data engineers, analysts, and BI developers across projects.
• Lead design reviews and enforce architectural consistency across data initiatives and engineering teams.
• Define and implement Fabric best practices for performance tuning, storage optimization, and cost governance.
• Establish strong data security models, including RBAC, encryption, data masking, and secure access patterns.
• Monitor Fabric platform health and proactively manage risks, ensuring high availability and operational resilience.

Candidate Profile

• 7+ years of experience in data architecture, data engineering, or analytics platform design.
• Strong hands on experience with Microsoft Fabric and related Azure services.
• Deep understanding of insurance data domains (policy, claims, underwriting, actuarial, billing).
• Expertise in data modeling (dimensional, canonical, semantic models).
• Strong knowledge of data governance, metadata management, and data quality practices.
• Proficiency with SQL, Python, and modern data engineering frameworks.
• Excellent communication and stakeholder management skills.
• Experience with Microsoft Purview, Power BI, Databricks, or Azure Synapse.
• Familiarity with modern data architecture concepts (data mesh, data fabric, Lakehouse).
• Certifications such as Azure Data Engineer
• Prior experience in P&C or Life insurance data modernization programs.